Input: Claim: X-ray crystallography can be used to determine the 3D structures of proteins. [SEP] Text: 9:357-359 Le SQ, Gascuel O. 2008. An improved general amino acid replacement matrix. and Evolution 25:1307-1320 -M, Leung C-M, Ting H--W. 2016. v1. 0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 102:3-11. 2014. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923-930. 1995. An estimate of global primary production in the ocean from satellite radiometer data. Journal of 17:1245-1271 örster önig A, Liss T, Lüssmann -H. 2004. ARB: a software environment for sequence data. Research 32:1363-1371. 2010. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics 11:538. 2010. Assembly algorithms for next-generation sequencing data. Genomics 95:315-327. 2015. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. 25:1043-1055. 2010. FastTree 2—approximately maximum-likelihood trees for large alignments. 5:e9490 Pruesse E, Peplies J, Glöckner FO. 2012. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28:1823-1829 Sharon I, Banfield JF. 2013. Microbiology. Genomes from metagenomics. Science 342:1057-1058 Shimodaira H, Hasegawa M. 1999. Multiple comparisons of log-likelihoods with applications to phylogenetic inference. and Evolution 16:1114-1116 -’--Llonch M, Sarmento H, Vieira--. 2015. Ocean plankton. Structure and function of the global ocean microbiome. Science 348:1261359. 2011. Next generation sequence assembly with AMOS. in Bioinformatics 11. 8. 1998. Prokaryotes: the unseen majority. Proceedings of the of Sciences of the of America 95:6578-6583 Yang Z.

Output: pass


Input: Claim: Coral reefs are affected by ocean acidification and warming. [SEP] Text: Based on our synthesis of the literature, we propose that the indirect effects of greatest concern are that a decline in CCA, and the settlement cues and habitat they provide, is likely to drive a reduction in recruitment of corals and impede population replenishment after disturbances. We suggest that manipulative field experiments, such as controlled seeding of coral larvae at naturally acidified locations and monitoring of settlement success and post-settlement survival compared with nearby control locations, are needed to resolve existing data gaps. Moreover, declining CCA will lead to diminished levels of reef cementation which can cause coral mortality due to substratum instability (Madin et al. 2012). Detailed monitoring of coral survival after storms at naturally acidified locations compared with nearby control sites, accompanied by measurement of CCA abundance and substratum strength, will provide new insights about how CCA loss affects coral survival. In addition to changes in CCA, increased bioerosion is likely to compound effects of decreased calcification and increase dissolution, to shift corals from net skeleton accretion towards net skeleton loss. While as yet there is limited evidence of a significant direct effect on coral abundance and diversity resulting from reduced structural complexity, a recent study indicates that coral biodiversity is highest at intermediate roughness (rugosity) of reef substrata (Torres-Pulliza et al. 2020). Field-based observations of the magnitude of bioerosion, and the diversity and abundance of bioeroding taxa, are required to link changes in bioerosion to differences in structural complexity on reefs, and to link these changes to coral diversity. A loss of structural complexity on reefs due to OA (via decreased cementation and increased erosion) is also likely to result in changes to reef functions more broadly. More complex reefs support higher diversity and abundance of fishes and therefore OA could affect the critical reef ecosystem services of food provision. Our review highlights the need to extend from single species studies, to studies that explicitly characterise how species interactions change, and how trophic linkages are altered, as pCO2 increases.

Output: corroborate


Input: Claim: Coral reefs are highly sensitive to changes in water temperature and acidity. [SEP] Text: There were sufficient data to consider the relationships between median spawning month of two species, Acanthopagrus australis (four records) and Pagrus auratus (six records), and SST and photoperiod at a smaller spatial scale, although the data sets were too small for statistical analysis. ResultsWhen data for all species are viewed at a family level, deviations in median spawning month from month of minimum SST are distributed fairly evenly before and after the month of minimum SST (Fig. 2). For both the Northern (Fig. 2a) and (Fig. 2b) [and consequently for the combined data set (Fig. 2c)] there is a clear decrease in the deviation of median spawning month away from the month of minimum SST, as mean annual SST increases. At mean annual SSTs of about 20°C and below, the deviation in median spawning month of sparids away from the month of minimum SST varies greatly, with deviations up to 6 months. In contrast, for mean annual SSTs of 21°C and above, the maximum deviation from the month of minimum SST is 3. 5 months, with only 3 deviations greater than 2. 25 months from a total of 41 data points (Fig. 2c). The reduction in deviation is significant. Quantile regression analysis (Scharf et al. 1998) indicated significant reductions in both the upper (80% quantile: y = 9. 8 − 0. 30x, P < 0. 002, n = 88) and lower (20% quantile: y = −8. 0 + 2. 50x, P = 0. 022, n = 88) boundaries of the data with increasing SST (Fig. 2c). Furthermore, the mean of the absolute value of deviation in spawning month decreased significantly with increasing SST (Table 2; Fig. 3a), reflecting the reduction in deviation with increasing minimum SST. This relationship was strong, as indicated by the high r 2 (0. 7677) and large F-value (307. 43). Fig. 2Deviation in sparid mean spawning month away from the month of minimum sea surface temperature (SST). Solid lines on part c represent 80 and 20% quantile regressions describing upper and lower boundaries of the data cloud for SST < 21°C.

Output: contrast


Input: Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: Biosynthesis of fragrance 2-phenylethanol from sugars by Pseudomonas putida access Published: 02 April 2024 Biosynthesis of fragrance 2-phenylethanol from sugars by Pseudomonas putida Patricia Godoy1, Zulema Udaondo2, Estrella Duque1 & …Juan L. Ramos1 Show authors Biotechnology for Biofuels and Bioproducts 2. The two strains without the pPE-1 plasmid did not produce 2-PE as expected (not shown). We found that the P. putida CM12-5 (pPE-1) and P. putida CM12-5Δgcd (xylABE) (pPE-1) produced about 50 ppm 2-PE when glucose was the growth substrate (Fig. 2). However, with xylose, negligible 2-PE accumulated, i. e. , less than 3 ppm was detected in the culture medium (not shown). When glucose and xylose were present simultaneously, a production level of 2-PE similar to that obtained with glucose alone was achieved (Fig. 2). These strains accumulated low amounts of phenylalanine in the culture medium (< 3 ppm) under all growth conditions (not shown), which suggests that almost all phenylalanine produced intracellularly was channeled towards 2-PE production. Fig. 2Production of 2-PE in the presence of glucose (open bars) or mixture of glucose:xylose (closed bars) as the C source. P. putida CM12-5 (pPE-1) and CM12-5Δgcd (xylABE) (pPE-1) cells were grown overnight with glucose as the C source. The cultures were then washed and diluted to DO660 of 0. 1 diluted in fresh medium with glucose (0. 5% w/v) or glucose:xylose (0. 375% w/v:0. 125% w/v) as the C source, and 2-PE production was measured after 24 h incubation.

Output: pass


Input: Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: Voice of the Publisher > Vol. 9 No. 4, December 2023 Importance of Models in Detection (). DOI: 10. 4236/vp. 2023. 94017 132 Downloads 704 Views 215. doi: 10. 4236/vp. 2023. 94017. 1. healthcare industry is open to the most significant industries in the society. It is an organization that plays a pivotal role in society, aiming to provide quality medical services. However, despite the benefits, the industry is also raided by numerous challenges. Healthcare fraud has been a significant challenge that has to be addressed. There are numerous fraudulent activities within healthcare Fernando, Gammulle, Denman, Sridharan, & Fookes (2021). These problems present financial implications, and they also jeopardize the well-being of patients. Consequently, they also erode the trust within the system. Many research works have stated that fraudulent activities in the healthcare sector costs billions of dollars annually and are a persistent concern for healthcare providers, according to Johnson & Activities (2019). In this era of data-driven decision-making and technological advancements, healthcare fraud detection has gained prominence. According to ( Springer (n. d. ) of and Analytics), the growing interest in statistical methods for data science spans across various disciplines, including statisticians, computer scientists, computational mathematicians, and physicists, emphasizing the importance of interdisciplinary collaboration. Many cybersecurity crimes are also on the rise. There have been traditional rule-based approaches that have proven inadequate in identifying sophisticated and evolving fraud schemes, according to the work of Nassif, Talib, Nasir, & Dakalbab (2021). Nonetheless, the healthcare industry is increasingly turning to advanced technologies, particularly machine learning, and they are supposed to enhance its fraud detection capabilities. Machine learning models offer improvement when it comes to the accuracy and efficiency of fraud detection in healthcare. The models can analyze vast volumes of structured and unstructured data. They have also been known to identify patterns and anomalies that are often elusive to human reviewers. According to Dissanayake, Fernando, Denman, Sridharan, Ghaemmaghami, & Fookes (2020) machine learning enables continuous learning from new data, and it allows the process of fraud detection systems in relation to threats. 2. Materials and success of machine learning-based healthcare fraud detection relies heavily on the availability and preprocessing of relevant data.

Output: corroborate


Input: Claim: Coral reefs are highly sensitive to changes in water temperature and acidity. [SEP] Text: 1997, 1998a, b, 1999, 2000, 2001, 2002a, b). In these areas, the spawning season of P. auratus deviates little from the month of minimum SST. Thus, by utilising winter spawning, tropical sparids can spawn over similar temperature ranges to more temperate species and/or populations, albeit at the cost of a shortened reproductive period. Tropical sparids such as A. berda and A. australis occur in the same locations (estuaries) throughout the year (Sheaves 1992, 1993, 1998), thus the temperature ranges experienced at these locations remain within adult tolerance levels. Despite this, both species spawn in winter, and in increasingly warmer waters the timing of spawning of A. australis moves progressively closer to the month of minimum SST. This, together with the family-wide relationship between SST and spawning season, suggests that thermal requirements for gamete development, or gamete, egg or larval survival, rather than thermal requirements of adults, may play an important part in determining the warm water limits of distribution of these species in particular, and sparids in general. If adults possess the physiological ability to withstand high temperatures but gametes, larvae, etc. , do not, targeting spawning at times of minimum water temperatures is an obvious tactic to allow a species to maximise penetration into warmer waters. In essence, tropical sparids may be members of a temperate group, limited in distribution by the temperature tolerances of gametes, eggs or larvae, but maximising their penetration into conditions at the upper limits of their thermal tolerance by targeting spawning to times of low water temperature. Although direct cueing of the timing of reproduction on water temperature to allow the production of gametes, eggs, larvae or juveniles when physical conditions are appropriate is an intriguing and plausible reason for the winter spawning of warmer water sparids, it is not the only possible explanation. Rather than a direct cue to accommodate the physiological needs of early stages, water temperature has often been suggested as a proximate cue that initiates gonad maturation and spawning, so that larvae are produced at times when their planktonic prey are abundant (Bye 1984). For example, the timing of spawning of the osmerid, Mallotus villosus, is related to warming sea temperatures that lead to production of larvae when conditions are optimal for zooplankton (Carscadden et al.

Output: contrast


Input: Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: The figure shows the average of three independent assaysFull size imageSince xylose is metabolized through the pentose phosphate pathway (PPP), it is likely that phosphoenolpyruvate (PEP) levels in CM12-5Δgcd (xylABE) are limiting the operation of the shikimate pathway (Fig. 1), which is critical for production of l-phenylalanine and its subsequent channeling towards 2-PE. Effect of the addition of the Ehrlich pathway and shikimate pathway intermediates on 2-PE productionTo determine if the limiting factor in the process was related to the production of the primary substrate, l-phenylalanine (l-Phe), or pathway intermediates, i. e. , phenyl acetaldehyde (PA) and phenylpyruvate (PP), we carried out resting cells assays in which 1 mM l-Phe (165 ppm), 1 mM PA (120 ppm) or 1 mM PP (186 ppm) were added to cultures of CM12-5 (pPE-1) and CM12-5Δgcd (xylABE) (pPE-1), in a medium with glucose or with xylose as the C source. Figure 3 shows that, regardless of the C source used (glucose or xylose), 2-PE production reached concentrations of about 100–120 ppm 2-PE in both strains, increases that represent production about 200% higher than those obtained in the absence of Ehrlich pathway intermediates. Therefore, the transformation of l-Phe into 2-PE through the Ehrlich pathway was not compromised. This suggests that the initial amount of l-Phe in the cells is the main limiting factor in the production of 2-PE. Fig. 3Production of 2-PE by resting cells in the presence of Ehrlich pathway intermediates: l-phenylalanine (l-Phe), phenylacetaldehyde (PA) or phenylpyruvate (PP). CM12-5 (pPE-1) (blue bars) and CM12-5Δgcd (xylABE) (pPE-1) (red bars) cells were grown in the presence of glucose as the C source until reaching a DO660 of 1, the cultures were then washed and concentrated in 1xM9 + 0.

Output: pass


Input: Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: Adhering to ethical practices, the researcher made sure that our research met data privacy regulations. The study had a focus on safeguarding sensitive patient information and complying with relevant legal and ethical standards. Ethics also concerns the security and privacy of the information. 3. results section of this research presents the results of the machine learning-based healthcare fraud detection experiments. The performances are based on various algorithms and provide insights into the effectiveness of machine learning models in addressing healthcare fraud. 3. 1. The research evaluated the performance of four machine learning algorithms: (XGBoost), and (CNN and RNN). Below is a summary of the key performance metrics of these models. The precision of a model assesses its proficiency in accurately classifying fraud cases, while recall quantifies the degree to which it can successfully identify every confirmed case of fraud. The F1-score is a performance measurement that considers both precision and recall. metric showcases the level of distinction a model can achieve between fraudulent and non-fraudulent transactions. The data from Table 1 shows that the machine-learning methods beat as a benchmark. The ensemble methods, and XGBoost, are shown to have high precision and recall, suggesting their ability to identify a significant number of fraud cases while minimizing false alarms. By focusing on CNNs and RNNs, deep learning reaches significant precision and recall scores of over 95%. 3. 2. To gain insights into the factors contributing to fraud detection, we analyzed feature importance scores for the and XGBoost models. Figure 1 illustrates the top ten most important features identified by both models. Table 1. Model performance metrics. Source: Srivastava (2023) Link ( Ariwala, 2023 ) Link, (Updated on Apr 05/2023). Figure 1. Illustrates the top ten most important features identified by both models. 3. 3. With a sensitivity analysis, we calculated model robustness by introducing synthetic variations in the dataset. Our models consistently displayed performance stability across different variations, suggesting their adaptability to rapidly changing fraud patterns. As stats in Figure 1, Machines are much better than humans at processing large datasets. They are able to detect and recognize thousands of patterns on a user’s purchasing journey instead of the few captured by creating rules. 3. 4. Real-Deployed within a healthcare provider’s fraud detection system in real-world applications, our best-performing model is the CNN.

Output: corroborate


Input: Claim: All coral reefs can be fully restored through artificial means. [SEP] Text: IntroductionCoral populations worldwide are sensitive to a range of biotic and abiotic pressures (Riegl 2020), many of which will be exacerbated by future climatic change and human exploitation (Wolff et al. 2018; Riegl and Glynn 2020). Subsequently, the future status of coral reef ecosystems relies on the resilience of coral populations, specifically their capacity to endure, and recover from, increasingly frequent disturbance events (Holling 1973). Accordingly, a resilience-orientated focus is becoming commonplace in reef management (Mcleod et al. 2019, 2021), necessitating an ability to predict the responses of coral populations to varying biotic and abiotic regimes (Lam et al. 2020). Yet, a limited understanding for the dynamics underpinning coral population performance and resilience still hinders our capacity to recognise and manage the climate induced reassembly and/or collapse of coral reef ecosystems (Bellwood et al. 2004; Edmunds and Riegl 2020). Although limited, the application of demographic theory within coral research is not a novel concept (Edmunds et al. 2014). Structured population models offer insight into how the state of individuals (typically their size, age, or developmental stage; Caswell 2001) arbitrates their survival and reproduction and how these fitness components in turn shape the overall responses of populations to local biotic and abiotic conditions (Box 1; Benton et al. 2006). Understanding how patterns in the survival, progression (i. e. growth), retrogression (i. e. negative growth, shrinkage; Salguero-Gómez and Casper 2010), and reproduction of individual colonies, influence population-level characteristics will improve effective management (Riegl et al. 2018; Brito-Millán et al. 2019a; Madin et al. 2020; Pisapia et al. 2020). Appropriately, state-structured demographic approaches are being increasingly utilised to quantify the mechanisms supporting or preventing the persistence and revival of disturbed coral communities (e. g. Kayal et al. 2018; Cant et al. 2021c). Accordingly, there is a growing appreciation for how the size structure of coral populations influences their dynamics and endurance (Bak and Meesters 1998, 1999; Anderson and Pratchett 2014; Dietzel et al. 2020; Lachs et al.

Output: contrast


Input: Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: 5% (w/v) glucose or xylose to reach a DO660 of about 10, and the effect of 1 mM l-Phe PA or PP supplements on 2-PE production was measured after 24 h of incubation. The figure shows the average values of three independent assaysFull size imageOn the other hand, l-phenylalanine in P. putida is made through the shikimate pathway, in which shikimate and chorismate are intermediates (Fig. 1), and hence we tested whether the addition of 1 mM shikimate (174 ppm) or 1 mM chorismate (226 ppm) would also lead to an increase in 2-PE production. Supplementation with chorismate to CM12-5 (pPE-1) and CM12-5Δgcd (xylABE) (pPE-1) growing with glucose or xylose (Table 1) led to production levels of about 1 ppm 105 ppm 2-PE (Table 1), a value similar to that reached when Ehrlich pathway intermediates were added. However, this was not the case with shikimate, as it was only partially consumed by the cells, and also because it was biotransformed in side dead-end products (3-hydroxyshikimic acid, quinate and acetate) both with glucose or xylose as the C source (data not shown). This implies that an appropriate channeling of shikimate to chorismate is a critical issue in the synthesis of 2-PE by this host platform. Hence, our data support the hypothesis that the intracellular levels of l-Phe maybe the limiting step in the biosynthesis of 2-PE. Moreover, this was particularly noticeable, when xylose was used as the sole C source, since with this C-source a clear limitation of the available intermediates for the synthesis of l-Phe was identified. Table 1 Effect of shikimate pathway intermediates chorismic acid (CA) and shikimic acid (SA) on the synthesis of 2-PE in P. putida CM12-5 (pPE-1) and CM12-5Δgcd (xylABE) (pPE-1) strainsFull size tableIncreasing phenylalanine production and 2-PE levels in P.

Output: pass


Input: Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: Over six months, it detected 92% of fraudulent claims with a low false-positive rate, significantly reducing financial losses. 4. this section, the researcher interprets the results presented in the previous section and discusses their implications for healthcare fraud detection. It is, therefore, important to address the broader context of machine learning adoption in the healthcare industry. 4. 1. our study, machine learning models, particularly ensemble methods and deep learning models, significantly outperformed the baseline model in detecting healthcare fraud. Precision and recall scores that are high demonstrate the effectiveness of these models in recognizing fraudulent activities ( Chalapathy & Chawla, 2019 ). In healthcare fraud detection, high precision values are crucial for minimizing false positives and reducing the burden on investigators ( Naidoo & Marivate, 2020 ). High recall values reflect the models’ ability to capture a significant amount of actual fraud cases, contributing to fraud prevention and financial savings. The implications of these outcomes showcase how machine learning can reshape fraud detection within healthcare settings. 4. 2. importance scores from the analysis showed that claim type, diagnosis codes, and provider specialty significantly impact fraud detection. The importance of these features is emphasized when developing fraud detection algorithms and implementing fraud prevention strategies Dissanayake, Fernando, Denman, Sridharan, Ghaemmaghami, & Fookes (2020). Healthcare organizations can reinforce their fraud detection systems by paying attention to specific features. 4. 3. analysis in our research showed how machine learning models are flexible enough to handle varying fraud patterns. With healthcare fraud schemes changing and adapting, an important characteristic is having the ability to evolve as well Nassif, Talib, Nasir, & Dakalbab (2021). The progressively learning abilities of machine learning models make them well-suited for identifying and preventing healthcare fraud. 4. 4. Real-practical use of our best-performing model, the CNN, was showcased when paired with a real-world healthcare provider’s fraud detection system as part of our research. The model performed exceptionally well in identifying fraudulent claims while keeping false positives to a minimum, leading to major savings in financial losses ( Nassif et al. , 2021 ). The successful real-world integration of machine learning into healthcare fraud prevention has shown tangible benefits. 4. 5.

Output: corroborate


Input: Claim: All coral reefs can be fully restored through artificial means. [SEP] Text: 2021), with recent appeals for efforts to be concentrated towards gauging the resilience of coral communities (Pisapia et al. 2020). However, we argue here that the contemporary application of state-structured demographic tools in evaluating the viability of coral populations, and their communities, does not reflect our need to anticipate their resilience. Box 1 Scaling from individuals to communitiesFull size tableFig. 1Records of survival, size, and reproductive output, collected from tagged coral colonies over time, can enhance our understanding of population trajectories and, when collected across multiple populations, can be used to predict community reassembly under future climate scenarios. Here, subscript notation is used to reflect how the changing state of individuals corresponds with changes in individual-level characteristics. In this schematic the state of individuals refers to colony size and thus the growth (g) of individuals between size x and y is expressed as gyxFull size imagePrevailing knowledge of coral population dynamics centres around long-term (i. e. asymptotic) population characteristics (Box 2), with measures of long-term population growth rate (λ) often used for projecting the viability of coral assemblages (e. g. Foster and Foster 2013, 2018; Edmunds 2015, 2020; Riegl et al. 2017; Kodera et al. 2020; Cant et al. 2021c; Shlesinger and van Woesik 2021). Indeed, the same is true across ecological theory, with our understanding of population dynamics focused on asymptotic population characteristics (Morozov et al. 2020). Quantifying the asymptotic characteristics of various populations presents a valuable comparative tool for exploring abiotic tolerances (Beissinger and Westphal 1998). Yet, for populations to realise their asymptotic potential, they require the persistence of stable conditions over extended timescales (Caswell 2001). In reality, though, seasonality exposes populations to periodic abiotic shifts, whilst predation, human exploitation, or storm and fire events generate recurrent and instantaneous disturbances. These short-term abiotic and biotic disturbances ensure that natural environments rarely exist at a stable equilibrium (Williams et al. 2011; Hastings et al. 2018). Consequently, evaluating the resilience and viability of populations requires an understanding for how the dynamics of populations manifest across timescales more representative of the disturbance cycles to which they are exposed (Hastings 2010).

Output: contrast


Input: Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: putida It has been described that l-Phe exerts feedback inhibition of its own synthesis through interaction of the amino acid with the R-domain of chorismate mutase/prephenate dehydratase, PheA, a bifunctional enzyme in the shikimate pathway for the synthesis of aromatic amino acids [34]. Molina-Santiago et al. [24] constructed PheAfbr, a PheA mutant variant in which the R-domain of PheA was deleted and the allosteric sensitivity to phenylalanine was eliminated. This PheAfbr mutant variant was expressed in plasmid pPHE1. This study confirmed that CM12-5 (pPHE1) accumulated higher titers of L-phenylalanine in the culture medium i. e. , up to 350 ± 10 mg L−1 versus around 300 ± 5 mg L−1 for CM12-5 (Fig. 4A). Then, CM12-5 (pPHE1) was transformed with pPE-1 and 2-PE measured in the medium. It was found that 2-PE increased from about 50–60 ppm to near 95 ± 5 ppm (Fig. 4B), production levels similar to those achieved with exogenous supplementation of l-Phe in resting cell assays. This confirmed that the limitation in the production of l-Phe is an obvious step in the production of 2-PE. Fig. 4l-Phe production and 2-PE production from 1. 5% (w/v) glucose of Pseudomonas putida CM12-5 derivative strains without and with the pPE-1 plasmid. Cells were grown overnight with glucose as the C source, then the cultures were diluted to DO660 of 0. 1 in fresh medium with 1. 5% (w/v) glucose, and l-Phe production (panel A) or 2-PE (panel B) was measured after 24 h incubation. The figure shows the average values of three independent assaysFull size imageThereafter, it was decided to search for clones that can produce more l-Phe than the CM12-5 and CM12-5 (pPHE1) strains. To this end, the P.

Output: pass


Input: Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: Data Privacy—Machine learning models have shown great promise in identifying healthcare fraud, yet there are still critical ethical and data privacy considerations that must be addressed ( Johnson & Khoshgoftaar, 2019 ). Regulatory standards and protection are crucial when handling patient data in healthcare organizations. Transparency and accountability in the use of machine learning models are essential for preserving public trust. 4. 6. Healthcare fraud detection offers opportunities for future machine learning research, especially in advanced techniques such as anomaly detection and reinforcement learning. Data scientists, healthcare professionals, and policymakers can work together interdisciplinary to develop fraud prevention strategies that combine technological solutions with regulatory measures Fernando, Gammulle, Denman, Sridharan, & Fookes (2021). The research emphasizes the vital part played by machine learning models in healthcare fraud detection. Healthcare organizations looking to address fraud, protect patient data, and uphold the integrity of the healthcare system find promising solutions in these models’ remarkable performance, adaptability, and real-world effectiveness. 5. this research, the report has explored the significance of machine learning models in healthcare fraud detection and their potential to revolutionize fraud prevention in the healthcare sector. 5. 1. have shown that machine learning models, specifically ensembles like and XGBoost, along with deep learning models such as CNNs and RNNs, significantly improve healthcare fraud detection effectiveness. By outperforming conventional approaches and achieving both high precision and recall scores, these models reliably identified fraudulent behaviour with low incidence of false positives. 5. 2. critical role of specific features, such as claim type, diagnosis codes, and provider specialty, in detecting healthcare fraud is highlighted by our analysis. Healthcare organizations looking to prevent fraud should prioritize these features, leading to more accurate fraud detection systems. 5. 3. learning models demonstrate robustness and adaptability to fraud pattern variations. Ongoing adaptation via new data lets them actively combat healthcare fraud effectively. The adaptability of these models keeps them effective despite shifting fraud strategies. 5. 4. Real-best-performing model, the CNN, deployed successfully in a healthcare provider’s fraud detection system, is an example of machine learning’s practical application in the real world. The effectiveness of this model is exemplified by its ability to lower financial losses while keeping the false-positive rate low for healthcare organizations. Conflicts of author declares no conflicts of interest regarding the publication of this paper. 0924.

Output: corroborate


Input: Claim: All coral reefs can be fully restored through artificial means. [SEP] Text: Anticipating the future condition and endurance of populations thus requires an awareness for their transient characteristics (Ezard et al. 2010). Transient shifts in the state structure of populations can elevate (amplification) or diminish (attenuation) their growth rates relative to asymptotic projections (Townley et al. 2007). Equally, for populations existing in a transient state, there is a natural tendency for their state structure to converge towards a stable distribution (Caswell 2001). Intuitively, with population resilience comprised of the attributes of resistance (the ability to withstand disturbances), compensation (the ability to benefit from disturbances) and recovery (the speed and ability to reattain stationary equilibria), the transient dynamics of populations therefore represent quantifiable measures of these features of resilience (Box 3; Hodgson et al. 2015; Capdevila et al. 2020). Box 3 Measuring transient population characteristicsFull size tableFig. 4Various measures exist for quantifying transient population characteristics. Disturbances (*) disrupt the structural composition of populations forcing them into a transient phases during which their trajectories will deviate from asymptotic expectations (λ, dashed line). The response of a population during this transient phase is contingent on their capacity for amplification (increases in population growth) and attenuation (declines in population growth). Using the measures of reactivity and maximal amplification (describing the initial, and maximum, increase in population growth rate during transient conditions) ecologists can quantify the relative amplification potential of natural populations. Alternatively, the measures of first-step attenuation and maximal attenuation can be used to describe the initial, and maximum decline in population growth rate during transient conditions. Meanwhile, damping ratio estimates describe the time required for a perturbed population to reobtain a stable equilibrium. Accordingly, by revealing the capacity for populations to grow (compensation), avoid declines (resistance), and/or recover following a disturbance event, these transient metrics present ecologists with measures of population resilienceFull size imageTransient dynamics and the resilience of coral communitiesTransient demographic approaches present an effective tool for explicitly evaluating the resilience of natural populations (Capdevila et al. 2020). Indeed, such approaches have proven instrumental in our understanding of the establishment of populations within new and variable environments (Iles et al. 2016; McDonald et al. 2016, 2017; Jelbert et al. 2019).

Output: contrast


Input: Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: putida CM12-5 and CM12-5 (pPHE1) strains were subjected to random mutagenesis with EMS and selection of clones resistant to high concentrations (> 3 mg mL−1) of the l-phenylalanine analogue p-fluoro-phenylalanine (FPA), the mutagenesis was carried out as described in Materials and Methods. One clone of each strain (named PG1E and PG2E), which were able to accumulate about 300 and 450 ppm L-Phe, respectively, when grown with 1. 5% (w/v) glucose (Fig. 4A) were kept. These overproducer strains were subsequently transformed with pPE-1 plasmid and 2-PE accumulation determined. With P. putida PG1E, around 100 ppm 2-PE accumulated and the highest levels of 2-PE were reached with PG2E that accumulated at close to 120 ppm (Fig. 4B). As the highest l-Phe and 2-PE productions were achieved with PG2E strain, it was decided to sequence its genome to determine which genomic changes were responsible for l-Phe overproduction. When comparing the genome of this l-Phe overproducer derivative PG2E with DOT-T1E genome, we found that, apart from the five mutations involved in l-Phe degradation pathways generated by direct mutagenesis and described in Molina-Santiago et al. [24], up to 400 SNP (see Additional file 2: Table S1) were found. To refine the study, the SNP in the genes that encode proteins related to l-Phe metabolism (Additional file 4: Table S3) were analyzed. A H431P change in shikimate dehydrogenase (QuiA) that may prevent the misrouting of shikimate to 3-hydroxyskimate was identified; together with a set of mutations in genes encoding enzymes of the phenylacetic acid (PAA) degradation pathway. As suggested by L324P in PaaH_2, Y154C in PaaK, V154A and F284L in PaaA (Additional file 3: Table S2). Indeed, due to the mutations in the paa genes, the PG2E strain lost the ability to metabolize PAA (see Additional file 1: Fig. S1).

Output: pass


Input: Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: [2] Ariwala, P. (2021). A for with Machine Learning. Maruti Techlabs. [3] Chalapathy, R. , & Chawla, S. (2019). for Anomaly Detection: A Survey. [4] Dissanayake, T. , Fernando, T. , Denman, S. , Sridharan, S. , Ghaemmaghami, H. , & Fookes, C. (2020). for Detection without Segmentation. IEEE Journal of Biomedical and Health Informatics, 25, 2162-2171. [5] Fernando, T. , Gammulle, H. , Denman, S. , Sridharan, S. , & Fookes, C. (2021). for Detection—A Survey. ACM (CSUR), 54, 1-37. [6] Johnson, J. M. , & Khoshgoftaar, T. M. (2019). . Journal of Big Data, 6, 63. [7] Naidoo, K. , & Marivate, V. (2020). Detection of. Design, Implementation and Use of Information and (10. 1007/978-3-030-44999-5_35 [8]. , & Dakalbab, F. M. (2021). for Anomaly Detection: A Systematic Review. IEEE Access, 9, 78658-78700. [9] Springer (n. d. ). of and Analytics. [10] Srivastava, T. (2023). 12 for Know (Updated 2023). Analytics Vidhya. [11] Tallón-Ballesteros, A. , & Chen, C. (2020). Explainable AI: Value to -Based Systems. , 332, 152.

Output: corroborate


Input: Claim: All coral reefs can be fully restored through artificial means. [SEP] Text: Exploring the transient dynamics of coral populations can thus enhance our understanding regarding the future diversity, condition, and resilience, of coral communities. Globally, coral communities are being exposed to increasingly recurrent thermal stress events, at frequencies soon expected to exceed the recovery time required by most coral species (Hughes et al. 2018; Sully et al. 2019). Presently, considerable value is placed on classifying coral species according to their morphological and functional traits, with the subsequent categorisation of coral taxa offering proxies for the vulnerability of various coral communities to said recurrent stress events (Darling et al. 2012, 2019). Quantifying the relative affinities of different coral populations for demographic recovery presents the opportunity for justifying these assumptions; helping to distinguish the species and populations most vulnerable and or resilient to projected recurrent disturbance regimes. Equally, the amplification and attenuation characteristics of populations simultaneously reflect their stability following a disturbance. Distinguishing between the differential attributes of compensation and resistance in different populations is important for managing the future condition of biological communities (Capdevila et al. 2020). Assessments of coral communities following repeated disturbances have demonstrated divergent shifts in species compositions (Adjeroud et al. 2009; Pratchett et al. 2011, 2020). With knowledge of the capacity for various coral populations to undergo demographic amplification or for avoiding demographic attenuation, we can begin to forecast and anticipate the reassembly of coral communities, thereby improving the effectiveness of adaptive reef management. Indeed, Sandin and McNamara (2012) and Brito-Millán et al. (2019b) demonstrate how the transient characteristics of coral populations determine the resilience of their wider reef communities to algal phase shifts. Importantly, these contributions to our understanding of transient dynamics within coral environments advocate for a consideration of spatial interactions within assessments of coral population dynamics. Spatial interactions between individual corals impacts upon their fitness characteristics (Buss and Jackson 1979; Idjadi and Karlson 2007; McWilliam et al. 2018), yet the exact role these spatial interactions play in defining the performance of coral populations remains unresolved (Alvarez-Noriega et al. 2018). Ultimately, assessing the combined influence of state-structured demography and spatial interactions (e. g. Adler et al. 2010; Chu and Adler 2015) upon the performance, and resilience, of coral populations represents an important and worthwhile endeavour.

Output: contrast


Input: Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: Therefore, the higher accumulation of l-Phe in the strain producing the highest l-Phe level, and the subsequent 2-PE obtained, may be related to a more efficient channeling of shikimate to l-Phe. The use of agricultural waste products as feedstocks for 2-PE productionTo explore the potential of Pseudomonas putida as a chassis to produce 2-PE from 2G substrates, the capability of producing 2-PE from corn stover (PCS) and sugar cane straw (PSCS) hydrolysates, prepared as described in Materials and Methods was assayed. To this end, P. putida CM12-5 (pPE-1), CM12-5(pPHE1) (pPE-1), CM12-5(pPHE1)-FPA (pPE-1) and CM12-5Δgcd (xylABE) (pPE-1) strains were grown in M8 minimal medium with KNO3 as the N source, and 3% (w/v) PCS or PSCS. 2-PE production levels were determined after 24 h and the results are shown in Table 2. All the strains produced between 82 and 110 mg L−1 2-PE, either from 3% PCS or 3% PSCS hydrolysates. The concentration of sugars was also considered in molar terms to estimate molar yields, which were in the range of 8. 6–11. 1% regarding glucose utilization (Table 2). Table 2 2-PE production from 2G lignocellulosic hydrolysates PCS and PSCS in Pseudomonas putida CM12-5 derivative strainsFull size tableSimilar assays were also done with all of these 2-PE producing strains in M9 minimal medium, but using 1G substrates as the C source, such as corn syrup, which was used at 1/80 (v/v). Our results are shown in Table 3. Surprisingly, the total production of 2-PE with 1G substrates was lower than with 2G substrates, since these strains accumulated between 10 and 84 mg L−1 of 2-PE and the molar yields were below 4. 8%.

Output: pass


Input: Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: in Finance and Economics 2023, Issue 4: 354-379. doi: 10. 3934/DSFE. 2023021 Previous 1. Centre for & Informatics, University, Potchefstroom, 2531, 2. for Theoretical and (NITheCS), Received: 16 June 2023 Revised: 14 August 2023 Accepted: 05 September 2023 Published: 13 October 2023 Text(HTML) the realm of machine learning, where data-driven insights guide decision-making, addressing the challenges posed by class imbalance in datasets has emerged as a crucial concern. The effectiveness of classification algorithms hinges not only on their intrinsic capabilities but also on their adaptability to uneven class distributions, a common issue encountered across diverse domains. This study delves into the intricate interplay between varying class imbalance levels and the performance of ten distinct classification models, unravelling the critical impact of this imbalance on the landscape of predictive analytics. Results showed that random forest (RF) and decision tree (DT) models outperformed others, exhibiting robustness to class imbalance. Logistic regression (LR), stochastic gradient descent classifier (SGDC) and naïve Bayes (NB) models struggled with imbalanced datasets. Adaptive boosting (ADA), gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and k-nearest neighbour (kNN) models improved with balanced data. Adaptive synthetic sampling () yielded more reliable predictions than the under-sampling (UNDER) technique. This study provides insights for practitioners and researchers dealing with imbalanced datasets, guiding model selection and data balancing techniques. RF and DT models demonstrate superior performance, while and NB models have limitations. By leveraging the strengths of RF and DT models and addressing class imbalance, classification performance in imbalanced datasets can be enhanced. This study enriches credit risk modelling literature by revealing how class imbalance impacts default probability estimation. The research deepens our understanding of class imbalance's critical role in predictive analytics. Serving as a roadmap for practitioners and researchers dealing with imbalanced data, the findings guide model selection and data balancing strategies, enhancing classification performance despite class imbalance. Keywords: credit risk, class imbalance, default prediction, supervised learning, classification Citation: Lindani Dube, Tanja Verster. Enhancing classification performance in imbalanced datasets: A comparative analysis of machine learning models[J].

Output: corroborate


Input: Claim: All coral reefs can be fully restored through artificial means. [SEP] Text: Alternatively, a disturbance does not necessarily imply a negative impact, merely a change to a system (Pickett and White 1985). Disturbances to the structure of populations can be brought about by the cessation of a continuous pressure (e. g. Götz et al. 2008), changes in resource availability (e. g. Goldstein and Steiner 2020), or their introduction into a novel environment (Jelbert et al. 2019). With the ability of natural populations for undergoing demographic compensation underpinning their capacity for exploiting changes in their local environment (Jelbert et al. 2019), the transient dynamics of populations can offer insight into their invasive potential (Iles et al. 2016). Demographic compensation mediates the persistence of coral populations established along coastlines characterised by increased abiotic variability (Cant et al. 2021a, b). Predicting and quantifying demographic compensation within coral populations will, therefore, aid in forecasting the winners and losers in reef communities targeted by conservation and restoration initiatives, or those undergoing climate induced range shifts. To illustrate this potential, we provide an example evaluating the performance of a subtropical coral population in (Box 4). Box 4 Transient dynamics in Pocillopora aliciae: an empirical exampleFull size tableFig. 5Describing how size mediated patterns in survival, growth, fragmentation, and recruitment, determine the transition of individual colonies during the time interval t to t + 1, we used an IPM to evaluate the condition and viability of a Pocillopora aliciae population. Repeated surveys of individually tagged colonies were used to document the survival, growth, fragmentation, and recruitment characteristics of individuals colonies, and how these patterns are influenced by colony size. By condensing these individual-level patterns into a probability density kernel our IPM subsequently translates the transition of P. aliciae individuals across a continuous size spectrum into measures of both asymptotic and transient population characteristicsFull size imageFig. 6The asymptotic and transient dynamics of Pocillopora aliciae within the forecast very different outlooks for this subtropical population.

Output: contrast


Input: Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: Production of 2-PE can be considered to take place in two sets of reactions: one that leads to synthesis of the primary substrate, L-Phe, and a second set that uses the Ehrlich pathway to produce 2-PE. It was found that, whether intracellularly made or exogenously supplied, L-Phe is stoichiometrically converted on 2-PE. The genes of the Ehrlich pathway were expressed in pPE-1 plasmid from the regulated Pm promoter [50, 51], whose expression is driven by XylS in the presence of alkylbenzoates in plasmid pPE-1, or from a constitutively expressed lac promoter in plasmid pPE-2 (not shown). Our unpublished results revealed that the regulated expression of the Ehrlich pathway yielded more consistent production than the constitutive expression, probably due to the generation of instability, as production decreased with time (P. Godoy, unpublished results). The limiting factor in the 2-PE production process was linked to the production of the primary substrate, phenylalanine (L-Phe). The limited flux toward the shikimate synthesis pathway seems to be the main bottleneck for the production of aromatic chemicals, because it is highly regulated at transcriptional and enzymatic levels [25, 52]. A key feature in a wide range of l-phenylalanine producers is the need to overcome the feedback inhibition by l-Phe of the bifunctional chorismate mutase/prephenate dehydratase (PheA) enzyme [24, 25]. This fact has been confirmed in this study, as the expression of the PheAfbr variant lacking the R-domain expressed in plasmid pPHE1 [24] led to significant increases in L-phenylalanine accumulation in the medium. Another step is to increase the availability of shikimate precursors phosphoenolpyruvate (PEP) and erythrose-4-phosphate (E4P) [25, 53,54,55,56,57]. This study shows that xylose is a less efficient C-source for l-Phe production than glucose, which is probably related to the low availability of PEP in cells growing on xylose, which limits the C flow towards the shikimate pathway.

Output: pass


Input: Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: in Finance and Economics, 2023, 3(4): 354-379. doi: 10. 3934/DSFE. 2023021 Related Papers: the realm of machine learning, where data-driven insights guide decision-making, addressing the challenges posed by class imbalance in datasets has emerged as a crucial concern. The effectiveness of classification algorithms hinges not only on their intrinsic capabilities but also on their adaptability to uneven class distributions, a common issue encountered across diverse domains. This study delves into the intricate interplay between varying class imbalance levels and the performance of ten distinct classification models, unravelling the critical impact of this imbalance on the landscape of predictive analytics. Results showed that random forest (RF) and decision tree (DT) models outperformed others, exhibiting robustness to class imbalance. Logistic regression (LR), stochastic gradient descent classifier (SGDC) and naïve Bayes (NB) models struggled with imbalanced datasets. Adaptive boosting (ADA), gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and k-nearest neighbour (kNN) models improved with balanced data. Adaptive synthetic sampling () yielded more reliable predictions than the under-sampling (UNDER) technique. This study provides insights for practitioners and researchers dealing with imbalanced datasets, guiding model selection and data balancing techniques. RF and DT models demonstrate superior performance, while and NB models have limitations. By leveraging the strengths of RF and DT models and addressing class imbalance, classification performance in imbalanced datasets can be enhanced. This study enriches credit risk modelling literature by revealing how class imbalance impacts default probability estimation. The research deepens our understanding of class imbalance's critical role in predictive analytics. Serving as a roadmap for practitioners and researchers dealing with imbalanced data, the findings guide model selection and data balancing strategies, enhancing classification performance despite class imbalance. 47. doi: 10. 31449/inf. v47i1. 4519 [2] (2022) Sentiment analysis on twitter data integrating textblob and deep learning models: The case of us airline industry. Knowl-255: 109780. doi: 10. 1016/j. knosys. 2022. 109780 [3] (2012) The'k'in k-fold cross validation. in 'ESANN', 441–446.

Output: corroborate


Input: Claim: All coral reefs can be fully restored through artificial means. [SEP] Text: 2012) within open-source programs like R (R 2019). Forecasting the resilience of coral populations presents a major challenge for the future conservation of various reef communities (Lam et al. 2020). Currently, we lack the mechanistic insight into the resilience and response of coral populations to disturbance necessary for predicting the future reassembly and persistence of coral communities given increasingly recurrent disturbances. It is not our intention to dismiss previous efforts to evaluate the dynamics of coral populations using asymptotic characteristics. The asymptotic characteristics of populations present a useful tool in exploring the relative tolerances of differing populations (Beissinger and Westphal 1998) and have proven valuable in the management of threatened populations (Johnson et al. 2010). We have illustrated, however, that for evaluating and forecasting the responses of coral assemblages to disturbance and changing environments, asymptotic characteristics do not represent measures of population resilience (Hastings et al. 2018). Instead, we advocate for greater a focus towards investigating the transient dynamics of coral populations that more accurately reflect their dynamics within natural environments (Ezard et al. 2010). Evaluating coral reef community resilience using the framework of transient demographic theory will provide a new effective lens on the temporal and spatial nature of coral reef resilience, a view that will transcend current approaches of conducting single reef assessments to determine resilience (Maynard et al. 2015; McLeod et al. 2021).

Output: contrast


Input: Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: In addition, while the Ehrlich pathway intermediates are stoichiometrically transformed into 2-PE, we observed that part of shikimate made in the cells may divert to dead-end 3-hydroxyshikimate. This was indeed confirmed when clones able to produce higher amounts of L-Phe were isolated upon a round of random chemical mutagenesis that resulted in the identification of a mutation in shikimate dehydrogenase that prevents 3-hydroxyshikimate accumulation, and limited metabolism through the phenylacetic acid degradation pathway. Lignocellulose from agricultural residues or from urban organic waste are expected to represent a relevant raw material for the production of chemicals. Thus, P. putida DOT-T1E and the l-Phe overproducing strains were engineered to bear the machinery involved in xylose uptake and catabolism and also for the 2-PE production genes. However, low production of l-Phe and 2-PE from xylose was found, in spite xylose being metabolized through the pentose phosphate pathway that generates E4P, which is a precursor of the shikimate pathway (see Fig. 1). Due to the fact that when l-Phe or Ehrlich pathway intermediates were added to cells grown of xylose, they were stoichiometrically converted to 2-PE by CM12-5 derivatives; channeling xylose to shikimate seems to be a key limiting step in 2G conversion of C5 sugars into 2-PE. Furthermore, PEP is also limiting due to its channeling to pyruvate that is eventually transformed into acetyl-CoA and enters the TCA cycle [25, 56, 57]. Taking all these facts into consideration, it is clear that it is necessary to re-organize the PEP-4EP metabolic processes when xylose is used as the C-source. It is known that 2G hydrolysates, in addition to sugars, contain a wide range of toxic chemicals such as furfural, 5-hydroxymethylfurfural and others ([30] and Additional file 4: Table S3). These toxic compounds are originated from partial degradation of sugars during steam-explosion in the intensive physicochemical pre-treatment of lignocellulosic material [30].

Output: pass


Input: Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: [4] Bentéjac C, Csörgő A, Martínez-Muñoz G (2021) A comparative analysis of gradient boosting algorithms. Rev 54: 1937–1967. doi: 10. 1007/s10462-020-09896-5 [5] (2015) Performance-weighted ensembles of random forests for predicting price impact. 15: 1823–1835. doi: 10. 1080/14697688. 2014. 983539 [6] (2021) A survey of machine learning in credit risk. J 17. = 3946261 [7] (2001) Random forests. Mach learn 45: 5–32. doi: 10. 1023/A:1010933404324 [8] (1984) Classification and regression trees (wadsworth, belmont, ca). 13: 978–0412048418. [9] (2015) Indoor localization in a hospital environment using random forest classifiers. Appl 42: 125–134. doi: 10. 1016/j. eswa. 2014. 07. 042 [10] (2010) On over-fitting in model selection and subsequent selection bias in performance evaluation. J Res 11: 2079–2107. [11] Chen T, (2016) Xgboost: A scalable tree boosting system, in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 785–794. [12] Chicco D, (2020) The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC Genomics 21: 1–13. doi: 10. 1186/s12864-019-6413-7 [13] (2018) Handling data irregularities in classification: Foundations, trends, and future challenges. 81: 674–693. doi: 10. 1016/j. patcog. 2018. 03. 008 [14] (2011) Bayesian networks classifiers for gene-expression data, in 2011 11th on Design and Applications, IEEE, 1200–1206. [15] (2018) Agricultural drought risk evaluation based on an optimized comprehensive index system. Sustainability 10: 3465. doi: 10.

Output: corroborate


Input: Claim: Most plastic in the ocean can be collected and recycled effectively. [SEP] Text: . org 2024 in to your account Login or this option if you have an account on informs. org or if you want to become an member Combine personal and institutional access with an Informs PubsOnline account using main navigation6, 2018 in Innovative Education: Recycling ocean plastic into the supply chain Student project: University of Michigan’s grad students team up with to address global problem : Share onShare on PRINT 10. 1287/orms. 2018. 04. 13 In sponsoring a 2017 student team project at the University of Institute for Global Operations, presented the challenge of developing an Asia-based ocean plastics supply chain for product packaging, designed to significantly reduce the total landed cost of ocean plastic by 73 percent over the current state. Dell has been an active sponsor of Tauber team projects since 1999, and is represented on the Tauber Institute’s Board by Piyush Bhargava, its vice president for global operations. team for this project consisted of grad students Dan Partin, who is working on Master of and Master of Environment and Sustainability degree, and Allison Ward, a member of the Program (EGL), which leads to BSE in Engineering and MSE in Industrial and degrees. The team’s challenge: identify and justify the best market for ocean plastic in Asia Dell’s packaging among its global markets. is one of the largest privately held global companies that provide a broad range of technology products for the consumer, education, enterprise and government sectors. In addition to its line of desktop and notebook PCs, Dell offers network servers, data storage systems, printers, Ethernet switches and peripherals such as displays and projectors. With the recent acquisition of is also now one of the biggest storage and enterprise services companies in the world. The supply chain is structured to deliver a holistic view of collection, resulting in reduced flow into the ocean. Source: Based in Austin, Texas, Dell has a rich history of supply chain and packaging innovation. As part of its commitment to ocean health outlined at the 2017 Ocean Conference, Dell pledged to source and incorporate 10 times its current annual ocean plastic usage into its packaging, amounting to up to 160,000 pounds annually, by 2025. More than 8 million tons of plastic enters the ocean each year, a growth rate that by 2050 would result in more plastic in the ocean than fish.

Output: contrast


Input: Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: The introduction of a mutant variant of chorismate mutase/prephenate dehydratase, bypassed feedback inhibition by aromatic amino acids, and significantly enhanced l-phenylalanine production and, consequently, boosted 2-PE yields. Furthermore, the application of random mutagenesis led to strains exhibiting elevated l-phenylalanine production, resulting in increased 2-PE production. In the realm of industrial applications, we explored the use of agricultural waste substrates such as corn stover, sugar cane straw, and corn syrup as potential carbon sources. Regardless of the waste source, glucose was the preferred sugar for 2-PE biosynthesis. To sum up, our study underscores the potential of microbial biorefineries, particularly those employing P. putida, as an environmentally sustainable alternative for producing valuable aromatic compounds like 2-PE. The implementation of strain engineering and the utilization of agricultural waste substrates are pivotal strategies identified for enhancing production efficiency and mitigating environmental impact. Materials and methodsChemicalsl-Phenylalanine (99%), 2-phenylethanol (99%), phenylacetaldehyde (90%), shikimic acid (99%), sodium phenylpyruvate (powder), chorismic acid barium salt (≥ 80%), o-toluic acid (99%), m-toluic acid (99%), p-fluoro-dl-phenylalanine (FPA) (98%), ViscozymeR l-cellulolytic enzyme mixture and ethylmethanesulfonate (EMS) were purchased from Sigma-Aldrich (Merck). Acetonitrile (HPLC grade), d-( +)-xylose and d-( +)-glucose were provided by VWR Chemicals (France). Strains, plasmids and growth conditionsStrains and plasmids used in this study are shown in Table 4. Pseudomonas DOT-T1E is a solvent-tolerant strain that was originally isolated from a wastewater treatment plant [8]. Pseudomonas putida CM12-5 is a DOT-T1E derivative that produced phenylalanine and it was previously described [24]. To make P. putida CM12-5 able to use xylose as a C-source, a derivative of this strain, bearing and xylE genes from E.

Output: pass


Input: Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: 3390/su10103465 [16] (2019) Extreme gradient boosting machine learning algorithm for safe auto insurance operations, in 2019 IEEE international conference on vehicular electronics and safety (ICVES), IEEE, 1–5. [17] (2018) Catboost: gradient boosting with categorical features support. arXiv preprint. doi: 10. 48550/arXiv. 1810. 11363 [18] (1992) The attribute selection problem in decision tree generation, in 'AAAI', 104–110. [19] (2021) Dynamically weighted balanced loss: class imbalanced learning and confidence calibration of deep neural networks. 33: 2940–2951. [20] Granström D, (2019) Loan default prediction using supervised machine learning algorithms. [21] (2012) Data mining concepts and techniques third edition, University of Illinois at Urbana-. [22] Ho TK (1995) Random decision forests, in Proceedings of 3rd international conference on document analysis and recognition, IEEE, 1: 278–282. [23] Kaggle (2023) Give me some credit. Available from: = cs-training. csv. Accessed: 2023-02-05. [24] (2017) Lightgbm: A highly efficient gradient boosting decision tree. Syst 30. [25] 'arcy A (2020) Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies, MIT press. [26] Khemakhem S, (2018) Predicting credit risk on the basis of financial and non-financial variables and data mining. Financ 17: 316–340. doi: 10. 1108/RAF-07-2017-0143 [27] (2015) Software defect prediction using ensemble learning on selected features. Tech 58: 388–402. doi: 10. 1016/j. infsof. 2014. 07. 005 [28] (2019) Machine learning in banking risk management: A literature review. Risks 7: 29. doi: 10. 3390/risks7010029 [29] (2022) Analysis and visualization of accidents severity based on lightgbm-tpe. Chaos, 157: 111987. doi: 10. 1016/j. chaos. 2022.

Output: corroborate


Input: Claim: Most plastic in the ocean can be collected and recycled effectively. [SEP] Text: We’d do that by extending the supply chain from Haiti to South Asia. ” Lessons learned along the way In addition to the obvious advantage of doing significant work at a top-tier company while still enrolled as students, Tauber team members indicated they learned the following lessons during the project: opened their eyes to the extent of the ocean plastics problem. It was very humbling to meet the informal collectors, and it put their heart behind the project even more. Ocean plastic goes beyond just beach clean-ups. The root cause is unmanaged waste infrastructure, which needs to be developed to fully address the problem. Many stakeholders, both internal and external, were affected by the supply chain. It was important to collaborate and gather perspectives from all parties. Addressing ocean plastic not only has an environmental impact, but also creates economic opportunities in Indonesia and India for collectors, processors and manufacturers. Dell is truly behind the ocean plastic cause and catalyzing industry change. The project goes beyond Dell by creating a working group to dramatically increase the impact on ocean health. The outcome: a new cost-effective supply chain and $350,000+ savings in one year. team’s proposed supply chain delivers scalability with the production capacity to source nearly 500 times Dell’s commitment of ocean plastic usage, or the equivalent of 1. 8 billion plastic bottles annually. It also reduced the total landed cost of ocean plastic by 73 percent over the current state, and by 31 percent and 16 percent against prevailing recycled and virgin plastic prices, respectively. Here are some of the ways the Tauber team reached these goals: Developed a scalable and cost-effective supply chain to source ocean plastic from Southeast Asia, reducing raw material costs up to 73 percent while diversifying the supplier network. Reduced manufacturing costs by 51 percent with a sourcing allocation strategy, allowing Dell to effectively remove the equivalent of 3. 6 million plastic bottles from the ocean annually. Collaborated with key stakeholders to analyze and recommend potential next use cases for Dell to exceed a public goal by 700 percent, totaling 1. 1 million pounds of recycled plastic annually. Tauber grad students (left) and defined a cost-effective supply chain for ocean plastic. Source: The scope of work described in the project had its challenges. Faculty advisor pointed out that, “Generally, operations projects begin with known inputs and a tried-and-true supply chain.

Output: contrast


Input: Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: coli that transports xylose into the cell and the xylAB, genes that encode enzymes to convert xylose into xylose-5 phosphate, which is metabolized through the pentose phosphate pathway, was constructed [45]. Efficient utilization of xylose required the inactivation of the glucose/xylose dehydrogenase (Gcd) to avoid the misrouting of xylose to dead-end xylonate [33]. A derivative of CM12-5 with Δgcd and bearing the xylAB and xylE genes was also available. Escherichia coli DH5α was used for the cloning experiments and propagation of plasmids (Table 4). Table 4 Strains and plasmids used in this studyFull size tableEscherichia coli was routinely grown at 37 ºC in LB medium, while the Pseudomonas putida strains were grown at 30 ºC in LB medium or in M9 minimal medium [60] with 5 g L−1 glucose as the sole carbon source. When indicated M8 minimal medium was used, this is identical to M9 medium except that ammonium chloride was replaced by 10 mM KNO3. For liquid cultures 100 mL conical flasks seeded with 20 mL culture medium were incubated in a Kühner thermostat incubator with agitation (200 rpm, 30 ºC). Growth was monitored by following the turbidity of the cultures at 660 nm (OD660) in a UV–VIS spectrophotometer Shimadzu 1900i (Kyoto, Japan). For resting cells assays, the cells were grown in M9 minimal medium with 5 g L−1 glucose as the sole carbon source, until the culture reached OD660 = 1. 0. Then, the cells were centrifuged and washed with 1xM9 and re-suspended in the appropriate medium at OD660 = 10. Pretreated sugar cane straw (PSCS) and corn stover (PCS) were sourced from the Plant in York, Nebraska, USA. The final dry matter content was determined to be 39. 9% for PSCS and 43. 3% for PCS, as reported in the compositional characterization by Rocha-Martín et al. [30].

Output: pass


Input: Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: Abstract Background: The use of intelligent decision support systems (IDSS) is widespread in the healthcare industry, particularly for real-time data, client and family history datasets, and prevalent patient features. Objective: A massive chunk of various kinds of health data sets, including sensor information, medical evidence, and omic statistics, are produced by the modern techniques in this field and eventually transferred to a machine learning (ML) element for extracting data, categorization, as well as mining. Method: In recent times, many patents have been focused on healthcare monitoring; however, they do not adequately incorporate appropriate algorithms for data collection, analysis, and prediction. The data collected is used for predictive modelling, then additionally, machine learning techniques are assisting to compare acquired datasets mathematically for decision-making platforms that may learn to recognise the recent trend and anticipated future problems. Depending on the dataset type, ML-based techniques can assess the circumstances. Training datasets are crucial for correctly anticipating both current and emerging events as well as new challenges. Results: Since the importance of data acquisition determines how well learning models function, any deformed data of the types of dirty data, noisy data, unstructured data, and inadequate information results in inaccurate detection, estimate, and prediction. Conclusion: Additionally, in contrast to other approaches, the experimental findings demonstrate the usefulness of the proposed method as a widespread implementation of machine learning algorithms within healthcare systems. Keywords: Healthcare, machine learning, latency, privacy, communication, intelligent system. 2020. [2] M. Saleem, and J. Sidiq, "Diagnosis and Classification of using Machine Learning-", Syst. Rev. , 2020. [3] H. K. Bhuyan, T. Arun Sai, M. Charan, K. Vignesh Chowdary, and B. Brahma, "Analysis of classification based predicted disease using machine learning and medical things model", 2022 Second international conference on advances in electrical, computing, communication and sustainable technologies (),. April 21-22, 2022, Bhilai, India, 2022. [ [4] K. Adapa, M. Pillai, S. Das, P. Mosaly, and L. Mazur, "Predicting objective performance using perceived cognitive workload data in healthcare professionals: A machine learning study", Stud. Health Technol. Inform. , vol. 290, 2022.

Output: corroborate


Input: Claim: Most plastic in the ocean can be collected and recycled effectively. [SEP] Text: This project had to concern itself with material quality questions, as well as a question of who would supply the ocean plastic. Navigating extra layers of complexity and uncertainty was a hallmark of the student performance and a testament to their determination and skill. ” team project at Dell benefited from exceptional individuals and interpersonal dynamics across the board. Partin and Ward took the challenge of their project and ran with it. “The students had to establish contact and engage themselves, relatively quickly, with a complicated reverse supply chain,” Skerlos adds. “They traveled to China, Indonesia and India, and created meaningful relationships that created fast iteration on supply chain opportunities. ” “Finding ocean plastic was only the first step in the journey,” Ward says. “We knew other important supply chain factors must be taken into consideration, such as cost and continuity of supply, but a key focus of our project was to deliver additionality. This meant we had to demonstrate that Dell’s intervention would make a positive impact on ocean health that would have not otherwise been created. To do so, we created a holistic strategy that would improve the existing waste management structure, targeting the root cause of the problem, not just a symptom. This approach considered the livelihoods and practices of informal sector collectors, local beach clean-ups and community outreach programs, and increased demand for low-value plastic. ” team first defined a cost-effective supply chain for ocean plastic that could be scaled to meet growing demand. Building on previous internal and external research, the team defined viable sourcing locations of ocean plastic based on a variety of factors, including the availability of mismanaged waste, processing infrastructure and logistical simplicity. Onsite visits in validated ocean plastic availability and confirmed the capability of local suppliers to source, clean and process the material. Pricing, capacity and quality certifications were modeled along with logistics, manufacturing costs, taxes and risk factors to define optimal supply chain scenarios for delivering ocean plastic resin to current Dell manufacturing sites in China, as well as scenarios that co-located manufacturing in regions with suppliers. for grad students (left) and onsite in South Asia, where ocean plastic pollution is a growing concern. Source: team went to Shanghai and Xiamen in China, as well as Chennai in India. The team then identified and recommended that Dell certify three Indonesian and Indian partners as viable sources of ocean plastic and co-locate packaging manufacturing in these countries.

Output: contrast


Input: Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: Following acid- and steam-explosion pretreatments, the soluble lignocellulosic substrates exhibited furfural contents ranging from 0. 31% to 1. 1%, and 5’-hydroxymethylfurfural contents ranging from 0. 23% to 0. 28%, as documented in the same study (30). PCS and PSCS were then hydrolyzed using (Sigma-Aldrich) as described by Godoy et al. [45]. The sugar content of these hydrolysates was 1. 7 ± 0. 1 g L−1 glucose and 2. 8 ± 0. 1 g L−1 xylose for PSC and 1. 5 ± 0. 1 g L−1 glucose and 1. 4 ± 0. 06 g L−1 xylose for PSCS. The strains were pre-cultured overnight in M8 minimal medium with 10 mM KNO3, as the N source, then, the cultures were diluted at 0. 1 OD660nm in M8 minimal medium with 10 mM KNO3 as N source without glucose and 3% (w/v) acid- and heat- pre-treated corn stover (PCS) or acid- and heat- pre-treated sugar cane straw (PSCS) [30]. Note that nitrate was used in these assays, because the pH of the culture medium remain along the fermentation test above 6. 1, at which growth of P. putida takes place, while when ammonium was the N source, the pH of the culture medium dropped to 4. 5–5. 3, which compromised the survival of the strain. Growth of the strains in this culture was monitored by determining CFU mL−1 in solid M9 medium. Corn syrup was also used as a source of carbon as indicated in the Results section. The corn syrup (purchased from Biosan, Tarragona, Spain) contained about 410 g L−1 glucose, 550 g L−1 fructose and 40 g L−1 sucrose. Isolation of l-phenylalanine overproducer P. putida mutantsputida, l-phenylalanine is produced through the shikimate pathway (Fig. 1).

Output: pass


Input: Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: [ [PMID: 35673130] [5] S. Verma, R. Popli, H. Kumar, and A. Srivastava, "Classification of thyroid diseases using machine learning frameworks", Int. J. Health Sci. , 2022. [ [6] J. Seo, T. H. Laine, G. Oh, and K. A. Sohn, "EEG-based emotion classification for alzheimer’s disease patients using conventional machine learning and recurrent neural network models", Sensors, vol. 20, no. 24, p. 7212, 2020. [ [PMID: 33339334] [7] L. Eloutouate, F. Elouaai, H. G. Tani, and M. Bouhorma, "Home automation and machine learning models for health monitoring", BDIoT 2021, 2021. [ [9] B. Koval, I. Khlevna, and S. Shabatska, Development of heart disease diagnosis concept using machine learning. , IT&I Workshops, 2021. [10] E. Shakeri, E. A. Mohammed, H. A. Z. Shakeri, and B. H. Far, "Exploring features contributing to the early prediction of sepsis using machine learning. " 43rd Annual international conference of the IEEE engineering in medicine & biology society (EMBC). Nov 01-05, 2021, Mexico, 2021. [11] A. Y. Darveshwala, D. Singh, and Y. Farooqui, "Chronic kidney disease stage identification in hiv infected patients using machine learning", 5th International conference on computing methodologies and communication (ICCMC). April 08-10, 2021, Erode, India, 2021. [12] and N. B. Warif, "Supervised and unsupervised machine learning for cancer classification: Recent development", IEEE international conference on automatic control & intelligent systems (I2CACIS). June 26-26, 2021, Shah Alam, Malaysia, 2021. [13] G. Shilimkar, A. Bhilare, and S. Pisal, "Disease prediction using machine learning", Int. J. Sci. Res. Sci. Technol. , 2021. [ [14] M. F.

Output: corroborate


Input: Claim: Fish populations can recover instantly if fishing is reduced. [SEP] Text: Fisheries‐induced evolution of alternative male life history tactics in Coho salmon Go to 11. Impacts of on Stocks and to 12. Drivers and uncertainties of forecasted range shifts for warm-water fishes under climate and land cover changeKristen L. Bouska, Gregory W. Whitledge, Christopher Lant, and Go to 13. Biochemical evolution in response to intensive harvesting in algae: Evolution of quality and quantity Go to 14. Ecology of harvest‐driven trait changes and implications for ecosystem management Go to 15. Rapid evolution meets invasive species control: the potential for pesticide resistance in sea lampreyErin S. . . Christie, Lori A. Criger, Julia L. M. Hinderer, Robert M. Hollingworth, Nicholas S. Johnson, Stephen R. . Morrison, David Mota-Sanchez, Andrew Muir, Maria S. Sepúlveda, and Michael P. to 16. Limited evolutionary responses to harvesting regime in the intensive production of algae Go to 17. Evaluating the sensitivity of ecological indicators with a perspective of temporal scales Go to 18. Effects of ambient oxygen and size-selective mortality on growth and maturation in guppies Go to 19. Roles of density-dependent growth and life history evolution in accounting for fisheries-induced trait changes Go to 20. Effects of Fishing on the to 21. Illegal tusk harvest and the decline of tusk size in the A frican elephant Go to 22. Vulnerability of individual fish to capture by trawling is influenced by capacity for anaerobic metabolism Go to 23. Sustainable management of freshwater crayfish (kōura, Paranephrops planifrons) in (Rotorua) lakes, North Island, Go to 24. Probabilistic maturation reaction norm trends reveal decreased size and age at maturation in an intensively harvested stock of pikeperch Sander lucioperca Go to 25. Bioeconomic consequences of fishing-induced evolution: a model predicts limited impact on net present valueFabian Zimmermann, Christian Jørgensen, and Go to 26. Statistical power: an important consideration in designing community-based monitoring programs for Arctic and sub-Arctic subsistence fisheries Go to 27. : on the importance of conserving old-growth age structure in fishery populations Go to 28.

Output: contrast


Input: Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: Mutants of CM12-5 and CM12-5 (pPHE1) strains that overproduce l-phenylalanine were generated using ethylmethanesulfonate (EMS) and selection of clones able to grow in the presence of > 3 mg mL−1 p-fluor-dl-phenylalanine (FPA), a toxic analogue of l-phenylalanine [61]. Briefly, cells were grown in M9 minimal medium with glucose until the mid-exponential growth phase was reached. Then, 10 aliquots of 100 μL of liquid culture were spread on M9 minimal medium solid plates with glucose as the C source and 3 mg mL−1 FPA, and left to dry for a few minutes. Then, a filter containing 30 mg of EMS was deposited on each plate and incubated at 30ºC. After 48 h, the colonies appearing within the inhibition halo were considered potential mutants and were picked and tested for growth in plates of M9 minimal medium with glucose and increasing concentrations of FPA (from 3 to 10 mg mL−1 FPA). Those mutants able to grow at high FPA concentrations > 3 mg mL−1 were selected as potential l-phenylalanine overproducers. One clone that tolerated 10 mg mL−1 FPA of each strain, and confirmed to produce l-phenylalanine, was chosen for further analysis. These clones were named PG1E and PG2E, the latter bearing the pPHE1 plasmid. Induction of the expression of Ehrlich pathway genes in pPE-1The construction of pPE-1 plasmid bearing the three genes involved in 2-PE synthesis through the Ehrlich pathway, i. e. histidinol phosphate transferase (PP_0967 from P. putida KT2440), phenylpyruvate decarboxylase (kdc from Rhodospirillum rubrum) and a native alcohol dehydrogenase (T1E_5478, adh from P. putida DOT-T1E) was described by Molina-Santiago et al. [24]. The three genes were cloned as an operon and their expression was driven from the XylS/o-toluic inducible Pm promoter [50] with 1 mM o-toluate.

Output: pass


Input: Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: Alrifaie, Z. Hussain, A. Shakir, and M. Lafta, "Using machine learning technologies to classify and predict heart disease", Int. J. Adv. Comput. Sci. Appl. , vol. 12, no. 3, p. 12, 2021. [ [15] D. M. Rao, D. R. Mohana, V. Talasila, and M. SureshKumar, "Prediction of chronic diseases at an early phase using machine learning approach", Research gate, 2021. [16] G. Luo, B. L. Stone, and M. D. Johnson, " et al. ,Automating construction of machine learning models with clinical big data: Proposal rationale and methods", JMIR Res. Protoc. , vol. 6, no. 8, p. e175, 2017. [17] G. Feretzakis, A. Sakagianni, and D. Kalles, " et al. , Using machine learning for predicting the hospitalization of emergency department patients", Stud. Health Technol. Inform. , vol. 295, 2022. [ [PMID: 35773897] [18] S. Samet, M. R. Laouar, I. Bendib, and S. Eom, "Analysis and prediction of diabetes disease using machine learning methods", Int. J. Decis. Support Syst. Technol. , vol. 14, no. 1, 2022. [ [19] A. R. Shenoy, and B. R K. , "Performance analysis of class imbalance handling techniques for early sepsis prediction using machine learning algorithms", 8th on and Systems (ICSSS), April 21-22, 2022, Chennai, India, 2019. [21] T. Ragupathi, and M. Govindarajan, "Performance assessment of different machine learning algorithms for medical decision support systems", Proceeding of the international conference on computer networks, big data and iot. Dec 19-20, 2019, Madurai, India, 2019. [22] T. Murari, L. Prathiba, and K. Kumar Singamaneni, " et al. , Big data analytics with oenn based clinical decision support system", & Soft Computing, vol. 31 2022, no. 2, 2021.

Output: corroborate


Input: Claim: Fish populations can recover instantly if fishing is reduced. [SEP] Text: Create a new account user Can't sign in? Forgot your username? Enter your email address below and we will send you your username Email* the address matches an existing account you will receive an email with instructions to retrieve your username close | or * Password* Forgot password? Reset it here Keep me logged in All fields with * are mandatory Email* All fields with * are mandatory Already have an account? Login here password must have 8 characters or more and contain 3 of the following: a lower case character, an upper case character, a special character or a digit password has been changed Can't sign in? Forgot your password? Enter your email address below and we will send you the reset instructions Email* the address matches an existing account you will receive an email with instructions to reset your password the verification code Cancel Congrats! has been verified close 10. 1139/F10-168 Get AccessContentsof Fisheries and Aquatic Sciences, Number 3March 20110. 128 of that for a population composed entirely of wild fish. The effect of hatchery fish on reproductive performance was the same among all three species. Further, the impact of hatchery fish from “wild type” hatchery broodstocks was no less adverse than hatchery fish from traditional, domesticated broodstocks. We also found no support for the hypothesis that a population's reproductive performance was affected by the length of exposure to hatchery fish. In most cases, measures that minimize the interactions between wild and hatchery fish will be the best long-term conservation strategy for wild populations. RésuméNous observons une relation négative entre la performance reproductive de populations naturelles de truites arc-en-ciel anadromes (Oncorhynchus mykiss), de saumons coho (O. kisutch) et de saumons chinook (O. tshawytscha) et la proportion de poissons de pisciculture dans les populations de reproducteurs. Nous utilisons la productivité intrinsèque estimée en ajustant une variété de modèles de recrutement aux données d'abondance de chaque population comme indice de performance reproductive.

Output: pass


Input: Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: Analytical methodsl-Phenylalanine, 2-phenylethanol (2-PE), and other aromatic intermediates present in culture supernatants were identified and quantified using an elution gradient protocol, with an Agilent 1260 HPLC–DAD system (see Godoy et al. [45]). l-Phenylalanine and 2-PE were both monitored at 215 nm, and their elution times were 5 and 10 min, respectively. Glucose, fructose and xylose consumptions were analyzed enzymatically as described by Godoy et al. [45]. Bio-informatic analysisputida PG2E genome was sequenced by Lda (Caparica, Portugal and (Madrid)). Single-nucleotide polymorphisms (SNPs) were identified by comparing sequencing reads from the PG2E strain against the P. putida DOT-T1E reference genome (GCA_026183455. 1) with Snippy v4. 6. 0 [62] that uses (0. 7. 17) [63] and Freebayes (v1. 3. 6) [64] with parameters, mincov = 10, minqual = 100 and mapqual = 60. Structural variants were analyzed using sorted bam files with Samtools v. 1. 16. 1 [65] and inspected using IGV v2. 15. 2 [66]. Availability of data and materials The data sets generated or analyzed during this study are included in this published 2001. 2020. ET. Ernst T. Fragrance chemistry : the science of the sense of smell. Academic Press; 1982. of biotransformation conditions for production of 2-phenylethanol by a Saccharomyces cerevisiae CWY132 mutant. Res. 2011;25:754–9. BMC Biotechnol. 2014. BMC Biotechnol. 2012;12:1–12. Value added products from fermentation of sugars derived from agro-food residues. Sci Technol. 2017;71:52–64. The future of petrochemicals—analysis - IEA. IEA, Paris License: CC BY 4. 0. 2018. $759 billion required for a net-zero petrochemicals sector by 2050 | BloombergNEF. BNEF. 2022. D, Gielen D.

Output: pass


Input: Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: [ [24] A. Karthikeyan, A. Garg, P. K. Vinod, and U. D. Priyakumar, "Machine learning based clinical decision support system for early COVID-19 mortality prediction", Front. Public Health, vol. 9, p. 626697, 2021. [ [PMID: 34055710] [25] M. R. Afrash, M. Yaghoubi, F. Rahimi, M. Shanbehzadeh, and M. Bahadori, "An intelligent system for prediction of severity of sarscov-2 infection and progression to critical illness: Using machine learning models", Researchgate. [26] R. Moore, K. R. Archer, and L. Choi, "Statistical and machine learning models for classification of human wear and delivery days in accelerometry data", Sensors, vol. 21, no. 8, p. 2726, 2021. [ [PMID: 33924388] [27] H. H. Alalawi, "Detection of cardiovascular disease using machine learning classification models", IJERT, vol. 10, no. 7, 2021. [28] M. Ganesan, and N. R. Sivakumar, "IoT based heart disease prediction and diagnosis model for healthcare using machine learning models", IEEE international conference on system, computation, automation and networking (). . March 29-30, 2019, Pondicherry, India, 2019. [ [29] S. Mishra, K. Shaw, and D. Mishra, "et al. , Improving the accuracy of ensemble machine learning classification models using a novel bit-fusion algorithm for healthcare ai systems", Front. Public Health, vol. 10, p. 858282, 2022. [ [PMID: 35602150] [30] A. Nikam, S. Bhandari, A. Mhaske, and S. Mantri, "Cardiovascular disease prediction using machine learning models",. Dec 16-18, 2020, Pune, India, 2020. [ [31] H. Mattie, P. J. Reidy, P. Bachtiger, E. R. Lindemer, M. Jouni, and T.

Output: corroborate


Input: Claim: Fish populations can recover instantly if fishing is reduced. [SEP] Text: L'importance de cette relation négative est telle que nous prédisons que la performance du recrutement dans une population composée entièrement de poissons de pisciculture serait de 0,128 celle d'une population composée totalement de poissons sauvages. L'effet des poissons de pisciculture est le même chez les trois espèces. De plus, l'impact des poissons de pisciculture provenant de stocks reproducteurs de pisciculture de « type sauvage » n'est pas moins négatif que celui des poissons de pisciculture de stocks reproducteurs traditionnels et domestiqués. Nous ne trouvons aucun appui à l'hypothèse qui veut que la performance reproductive d'une population soit affectée par la durée de son exposition aux poissons de pisciculture. Dans la plupart des cas, les mesures qui minimisent les interactions entre les poissons sauvages et les poissons de pisciculture constituent la meilleure stratégie de conservation à long terme pour les populations sauvages. Get full access to this 2008. Fitness of hatchery-reared salmonids fish in the wild. Evol. Appl. , 1(2): 342–355. CrossrefWeb of Science2004. Review of the relative fitness of hatchery and natural salmon. U. S. Dept. Commer. ,. No. NMFS-NFSC-61. 2004. The controversy about salmon hatcheries. Fisheries (Bethesda, Md. ), 29: 12–31. CrossrefWeb of Science2009. Using an unplanned experiment to evaluate the effects of hatcheries and environmental variation on threatened populations of wild salmon. Biol. Conserv. 142(11): 2449–2455. CrossrefWeb of Science2002. Model selection and multimodel inference: a practical information-theoretic approach. Springer-Verlag, New York, N. Y. 1996. Status review of west coast steelhead from Washington, Idaho, Oregon, and California. U. S. Dept. Commer. ,. NMFS-NWFSC-27. 2009.

Output: contrast


Input: Claim: Bioengineered yeast can produce biofuels from sugars. [SEP] Text: Zero-emission pathway for the global chemical and petrochemical sector. Energies. 2021;14:3772. 2021. Available from: H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. 2013. Available from: E, Marth G. Haplotype-based variant detection from short-read sequencing. 2012. Available from: The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9. 24. 75. 00. Author informationAuthors and AffiliationsDepartment of Environmental Protection, Estación Experimental del Zaidín, CSIC, c/ 1, 1808, & Juan L. RamosDepartment of Biomedical Informatics, University of Arkansas for 72205, author publicationsYou can also search for this author in PubMed 1. Comparative growth of P. putida DOT-T1E, CM12-5 and PG2E strains in the presence of glucose or phenylacetate as C source. Growth of the three P. putida strains was tested in M9 minimal medium with 0. 5% (w/v) glucose or 10 mM phenylacetate as C-source. Turbidity (right side) in liquid medium was measured 24 h after inoculation, while growth on solid medium was observed after 24 h incubation of streaked cells on solid M9 medium with either glucose or phenylacetate. Additional file 2: Table S1. SNPs found in P. putida PG2E genome with respect to P. putida DOT-T1E. Additional file 3: Table S2. Identification of mutated genes related with L-phenylalanine metabolism in P. putida PG2E as compared to P. putida DOT-T1E strain. Additional file 4: Table S3. Chemical composition of the soluble fraction of acid- and steam- explosion pretreated corn stover (PCS) and sugar cane straw (PSCS). Data are taken from Rocha-Martin et al. [30]. Rights and permissions This 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the licence, and indicate if changes were made.

Output: pass


Input: Claim: Machine learning models are used in many areas, including healthcare and finance. [SEP] Text: Panch, "A framework for predicting impactability of healthcare interventions using machine learning methods, administrative claims, sociodemographic and app generated data", Arxiv, 2019. & Patents on Engineering Title:in Healthcare using Models Volume: 18 Issue: 5 Author(s): Anup Patnaik*K. Affiliation: Institute of Engineering and : Healthcare, machine learning, latency, privacy, communication, intelligent system. Abstract: Background: The use of intelligent decision support systems (IDSS) is widespread in the healthcare industry, particularly for real-time data, client and family history datasets, and prevalent patient features. Objective: A massive chunk of various kinds of health data sets, including sensor information, medical evidence, and omic statistics, are produced by the modern techniques in this field and eventually transferred to a machine learning (ML) element for extracting data, categorization, as well as mining. Method: In recent times, many patents have been focused on healthcare monitoring; however, they do not adequately incorporate appropriate algorithms for data collection, analysis, and prediction. The data collected is used for predictive modelling, then additionally, machine learning techniques are assisting to compare acquired datasets mathematically for decision-making platforms that may learn to recognise the recent trend and anticipated future problems. Depending on the dataset type, ML-based techniques can assess the circumstances. Training datasets are crucial for correctly anticipating both current and emerging events as well as new challenges. Results: Since the importance of data acquisition determines how well learning models function, any deformed data of the types of dirty data, noisy data, unstructured data, and inadequate information results in inaccurate detection, estimate, and prediction. Conclusion: Additionally, in contrast to other approaches, the experimental findings demonstrate the usefulness of the proposed method as a widespread implementation of machine learning algorithms within healthcare systems. this page × Export File: RIS (for ) BibTeX Text Content: Citation and this 10. 2174/1872212118666230606145738 DOI Print ISSN1872-2121 2212-4047 this journal Call for Papers in Submission closes on : 03 February, 2025 Processing and The rapid evolution of computational information processing and edge computing has transformed numerous industries, enabling more efficient data processing, real-time analytics, and intelligent decision-making at the edge of networks. This special issue aims to gather cutting-edge research, innovative methodologies, and practical applications in these dynamic fields. We invite contributions that.

Output: corroborate


Input: Claim: Fish populations can recover instantly if fishing is reduced. [SEP] Text: One example is by fishing. Fishing selects fish with unique traits. First, fishers take large fish out of the water. So, populations become small and have only small fish left in them. These fish have babies for the first time when they are younger and smaller. Second, different fishing methods can select fish with different behaviors. So, fishers might catch more timid fish or more active fish, depending on the method. Fishing makes fish populations less diverse and productive. Less diverse means there are only small fish or active fish, not a mix of big, small, active, and timid. Less productive means fewer fish to catch later. Small, less diverse populations are weaker and cannot respond to large changes in the environment. Weaker populations are more difficult to fish in a way that will allow the population to survive. So, fishing makes the fish populations less valuable to the fishers. When you go fishing, you hope to catch the big fish. Big fish are better to show off to your friends (and enemies). Professional fishers prefer big fish, too. They get paid more for big fish, and it can be illegal to take small fish. Laws specify size limits to protect small fish, so they can grow and reproduce before fishers take them out. Laws protecting small fish help to make sure that fish stay abundant, so there will still be fish to catch in the future. What is the Although catching big fish seems to be the best thing to do, there are several problems with this strategy. First, protecting small fish does not guarantee that the fish numbers will remain high. Big fish have more offspring (babies) and their offspring are healthier. So, protecting only small fish can lead to smaller fish populations. There is a second problem. If we only remove big fish, we will run out of big fish! Offspring size depends on the size of the parents. Big fish will give birth to babies that can grow to become big fish. Small parents will have small offspring. Over time, the number of big fish in the population will be lower. Think about a cow farm. Farmers prefer cows with their favorite traits. Big, healthy, well-behaved cows that have many babies and give a lot of milk. Farmers will keep their favorite cows and the best bull to reproduce, to make sure they will have cows with the preferred traits year after year. But we are doing the opposite with the fish in the sea!

Output: contrast