Input: Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: This is due to the inflow of warmer Atlantic waters, which keeps the area ice-free 11,31,32. Furthermore, the Arctic is warming rapidly, with temperatures rising by up to 0. 5°C at its marginal seas. There is accelerated heat and energy transfer from the ocean to the atmosphere aided by the lower trend in SIC, and it is primarily linked to the AA 14. Moreover, the increase in temperature during cold seasons (autumn and winter) is one of the possible reasons for the amplified warming in the Arctic 28. Similarly, the present study found differential atmospheric warming and cooling in different seasons in the Arctic by analyzing the seasonal and decadal trends of T2M (Fig. 1i-l). We observed a significant positive T2M trend throughout the year, with the highest trend during cold seasons compared to the warm season. During the summer, most of the heat observed from the atmosphere is used to melt the sea ice, resulting in a lower surface temperature (Fig. 1k). On the other hand, the T2M trends in autumn are higher due to the underlying mechanism through which absorbed latent oceanic heat is released back into the atmosphere during the summer. This contrasting behaviour of seasonal T2M is often attributed to the accelerated melting of sea ice, which keeps the surface air temperature near freezing in boreal summer 33,34. The enhanced heat flow from the exposed open sea surface, rather than ice-albedo feedback, is responsible for amplifying temperature during the cold season 35. However, the Arctic SIC shows a significant negative correlation with the T2M in all seasons, with the highest correlation in autumn (Table S1). Furthermore, in the following section, we have attempted to determine the mechanism responsible for temperature amplification in the Arctic. in the of the seasonal variations in air temperature (AT) at the vertical level in the (30ºN-90ºN) are shown in Fig. 2. Warm seasons have a lower AT trend than cold seasons, which corresponds to the observations in the previous section. The question is to determine what drives significant warming in the mid- and upper Arctic atmosphere during the cold seasons. The temperature variation of the Arctic atmosphere on the surface and in the air aloft is influenced by the quantity of sea ice cover. As a result, during sea ice melting, surface amplification occurs14, resulting in stable atmospheric stratification at lower altitudes in the summer.

Output: contrast


Input: Claim: Bioinformatics can eliminate all genetic diseases in the future. [SEP] Text: Skip to main content Explore journals Get published About BMC My account Search all BMC 1. Though the length L is usually fixed to be a small value (e. g. , 13) in previous studies [2, 3], it is still infeasible to perform exhaustive search to check all potential phosphorylation motifs. Besides, it is also unclear which metric is more suitable to measure the statistical significance of the motif. To efficiently find phosphorylation motifs, two heuristic methods have been proposed. The Motif-X method [2] is a greedy algorithm that reports motifs in an iterative manner. In each round, one most statistically significant motif is detected. The peptides matching the motif identified in the first round are removed prior to the second round of searching. This procedure repeats a number of rounds until no significant motifs can be found. The MoDL method [3] is an optimization-based algorithm, which formulates the motif-finding problem as the minimization of description length. In other words, MoDL tries to optimize the expressiveness of a set of motifs rather than quantifying the significance of a single motif. However, both Motif-X and MoDL can only find a small subset of phosphorylation motifs. They cannot guarantee to find all statistically significant motifs so that some important phosphorylation patterns remain unknown to biologists. Furthermore, Motif-X is limited to discover non-overlap motifs while MoDL unusually reports at most three motifs. In this paper, we present a new algorithm called Motif-All for the discovery of all statistically significant phosphorylation motifs. Motif-All uses the odds ratio to quantify the over-expressiveness of each motif in the set of phosphorylated peptides against the background set. To avoid exhaustive search, we impose a support constraint for each motif on the set of phosphorylated peptides. The use of support constraint enables us to borrow ideas from association rule mining [4] to generate and prune candidates in a level-wise manner before calculating the odds ratio. To demonstrate the superiority of Motif-All method, we conduct experimental studies using the PhosPhAt database 3. 0 of Arabidopsis phosphorylation sites [5, 6]. Motif-All performs better than Motif-X and MoDL in finding more significant phosphorylation motifs.

Output: pass


Input: Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: The entomopathogen Beauveria bassiana has epiphytic and endophytic activity against the tomato leaf miner Tuta absolutaJ. Appl. Entomol. 2016; 140:580-589CrossrefScopus (0)18. Biswas, C. ∙ Dey, P. ∙ Satpathy, S. . . . Endophytic colonization of white jute (Corchorus capsularis) plants by different Beauveria bassiana strains for managing stem weevil (Apion corchori)Phytoparasitica. 2013; 41:17-21CrossrefScopus (50)19. Russo, M. L. ∙ Scorsetti, A. C. ∙ Vianna, M. F. . . . Endophytic effects of Beauveria bassiana on corn (Zea mays) and its herbivoreRachiplusia nu (Lepidoptera: Noctuidae) Insects. 2019; 10:110-119CrossrefScopus (0)20. Allegrucci, N. ∙ Velázquez, M. S. ∙ Russo, M. L. . . . Endophytic colonisation of tomato by the entomopathogenic fungus Beauveria bassiana: the use of different inoculation techniques and their effects on the tomato leaf miner Tuta absoluta (Lepidoptera: gelechiidae)J. Plant Protect. Res. 2017; 57:206-21321. Pourtaghi, E. ∙ Talaei-Hassanloui, R. ∙ Nasibi, F. . . . Endophytic colonization of tomato by Beauveria bassiana for control of the greenhouse whitefly, Trialeurodes vaporariorum (Hemiptera: aleyrodidae)Acta Biol. 2020; 14:149-160Crossref22. Gunatilaka, A. A. Natural products from plant-associated microorganisms: distribution, structural diversity, bioactivity, and implications of their occurrenceJ. Nat. Prod. 2006; 69:509-526CrossrefScopus (857)PubMed23. Ownley, H. B. ∙ Gwinn, D. K. ∙ Vega, E. F.

Output: corroborate


Input: Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: A previous study also found that AT in the Arctic has a highly stable and stratified vertical structure 3. During the spring season, the negative AT trend is confined to the upper atmosphere (∼250 hPa), which suggests that the influence of sea ice loss is constrained to the surface and lower levels of the atmosphere (Fig. 2a) due to limited vertical mixing 27. The summer AT trend recorded maximum warming in the mid-atmosphere relative to the lower and upper atmospheric AT (Fig. 2b). The positive ice-albedo feedback mechanism is more apparent during the summer. Increased atmospheric heat absorption by the ocean causes the open water to become warmer than the surrounding atmosphere. In all other seasons, mid-tropospheric warming is most likely due to an imbalance in horizontal energy transmission 28. The ocean's heat accumulated in the summer was released in the subsequent seasons, as observed in autumn (Fig. 2c). The AT trend in the mid-troposphere of the Arctic during autumn is approximately 0. 6°C per decade. The AT trend is positive during the cold seasons due to weak air stratification and greater vertical mixing. The upper troposphere (∼250 hPa) warms in the winter due to enhanced vertical mixing of heat and energy from the surface to the atmosphere aloft rather than ice-albedo feedback. Additionally, during the winter, the retreating sea ice and delayed freezing allow for the release of excess heat stored in the ocean 34,36,37. In the upper troposphere, a negative AT trend (60ºN-90ºN) is recorded in all seasons (Fig. 2a-c) except in winter, which shows a positive AT trend and winter warming (Fig. 2d). Arctic atmospheric warming above the surface might also be influenced by other possible factors such as cloud cover 38, increased downward LW radiation 35, heat and energy transfer from the lower latitudes, and differential radiative forcing 28. The following section investigated the role of heat fluxes associated with seasonal variations in Arctic sea ice cover. sea ice changes using and NHF This study examined the contribution of ocean-atmospheric heat fluxes to atmospheric warming and stability due to sea ice changes. Interannual and seasonal variations in sea ice cover were also investigated since they increase the interaction between the ocean, ice, and atmosphere.

Output: contrast


Input: Claim: Bioinformatics can eliminate all genetic diseases in the future. [SEP] Text: Furthermore, it is very fast and is able to finish the mining of large data sets within a reasonable time period. The rest of the paper is organized as follows: Section 2 presents the Motif-All algorithm. Section 3 shows the experimental results. Section 4 gives some discussions and Section 5 concludes the paper. MethodsThere are two critical issues in phosphorylation motif finding. The first is how to measure the over-expressiveness of each motif. The second is how to perform the search in an efficient manner. In the proposed Motif-All method, we use odds ratio to evaluate if one candidate motif is over-expressed. To search efficiently, we use the following strategies to improve the running efficiency of Motif-All. We impose a support constraint on each candidate motif. Here the support for a motif is defined as the percentage of phosphorylated peptides that match this motif. The notation of support is widely used in association rule mining [4]. One motif is said to be frequent if its support is no less than a given threshold. The aim of introducing support constraint is two-fold: on the one hand, it can filter out non-frequent motifs that may correspond to random artifacts; on the other hand, it makes it possible to generate and prune motifs in a level-wise manner so as to avoid brute-force search. We divide the mining process into two stages. In the first stage, we perform frequent motif finding using only the data set P since the number of phosphorylated peptides is much smaller than that of unphosphorylated ones. In the second stage, we collect support information using the data set N to calculate the statistical significance of those candidate motifs from the first stage. We will report all phosphorylation motifs whose p-values are no larger than a user-specified significance threshold. Odds ratio and statistical significance scoreThe odds of an event is the probability that this event occurs divided by the probability that it does not occur. The odds ratio is defined as the ratio of the odds of an event in one group to the odds in the complementary group [7]. In the context of phosphorylation motif discovery, the first group corresponds to the set of phosphorylated peptides P and the second group is the set of unphosphorylated peptides N. For a given motif m, we can construct a contingency table as shown in Table 1.

Output: pass


Input: Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: Endophytic fungal entomopathogens with activity against plant pathogens: ecology and evolutionBioControl. 2010; 55:113-128CrossrefScopus (305)24. Liao, X. ∙ Lovett, B. ∙ Fang, W. . . . Metarhizium robertsii produces indole-3-acetic acid, which promotes root growth in Arabidopsis and enhances virulence to insectsMicrobiology. 2017; 163:980-991CrossrefScopus (87)PubMed25. Batta, Y. A. Recent advances in formulation and application of entomopathogenic fungi for biocontrol of stored-grain insectsBiocontrol Sci. Technol. 2016; 26:1171-1183CrossrefScopus (27)26. Ibrahim, L. ∙ Dakache, C. ∙ Kreidy, M. E. . . . Environmentally sustainable production of Metrhizium anisopliae and Beauveria bassiana for control of Tuta absolutaInt. J. Agri. Biosyst. Eng. 2017; 2:1-1227. Shiberu, T. ∙ Getu, E. Entomopathogenic effect of Beauveria bassiana (Bals. ) and Metarrhizium anisopliae (Metschn. ) on Tuta absoluta (Meyrick) (Lepidoptera: gelechiidae) larvae under laboratory and glasshouse conditions in EthiopiaJ. Plant Pathol. Microbiol. 2017; 8:411-41428. Aynalem, B. ∙ Muleta, D. ∙ Venegas, J. . . . Morphological, molecular, and pathogenicity characteristics of the native isolates of Metarhizium anisopliae against the tomato leaf miner, Tuta absoluta (Meyrick 1917) (Lepidoptera: gelechiidae) in EthiopiaEgypt J. Biol. Pest Contr. 2020; 30:1-1129. Aynalem, B. ∙ Muleta, D. ∙ Venegas, J. . . .

Output: corroborate


Input: Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: The seasonal sea ice growth or retreat is driven by the ocean-atmospheric heat exchange process influenced by the THF and RHF. sea ice variation is determined primarily by dynamic and thermodynamic mechanisms 39. The dynamic process involves the movement and accumulation of sea ice to the south, eventually melting. On the other hand, thermodynamics deals with the exchange of heat and energy between the ocean, ice, and atmosphere. Since several other mechanisms function simultaneously, these processes are independent. The earlier study revealed that one-third of the long-term variations in the oceanic heat content in the Arctic result from the fluctuation in ocean-atmosphere heat flux 40. In response to thermodynamic processes, our study focused on the seasonal change in sea ice. The surface RHF and THF signs illustrate the direction of ice-ocean-atmosphere interactions. The RHF is governed primarily by the wind speed and AT near the surface, whereas integrals across the entire atmospheric column mainly determine the THF. Reduced sea ice can provide positive surface THF (upward), heat up the atmosphere, and indicate that ice drives the atmosphere. The negative THF (downwards) implies that the atmosphere drives sea ice. The negative NHF is associated with heat from the ocean and vice versa 41. Accelerated sea ice loss in the Arctic will result in more heat transfer from the ocean to the atmosphere, resulting in atmospheric warming amplification. A composite analysis was carried out to explain the heat flow process that contributes to atmospheric warming and ice melting. ice loss occurs throughout the year, but we considered the heat flux changes only in the negative SIC phase in this study. During the negative SIC phase in spring, the peripheral Arctic seas have negative anomalies, with only the central Arctic experiencing positive anomalies (Fig. 3a). The increasing insolation causes the sea ice to melt with the onset of spring, resulting in a positive THF across the Arctic Ocean, whereas the RHF and NHF are observed to be negative (Fig. 3b-d). A positive LH anomaly has been found in the central Arctic, while a greater negative SH anomaly remains across the Arctic region (Fig. S1). The positive LH and SH fluxes imply that sea ice transfers heat to the atmosphere 42. Spring SIC had a significant (p <0. 01) positive correlation with THF (r = 0. 67), RHF (r = 0.

Output: contrast


Input: Claim: Bioinformatics can eliminate all genetic diseases in the future. [SEP] Text: In this table, c00, c01, c10 and c11 are non-negative “cell counts” and denotes that the motif m doesn’t exist. Then, the calculation of odds ratio reduces to: (1) Table 1 A contingency table for a phosphorylation motifFull size tableAn odds ratio of 1 means that the target motif is equally likely to exist in both P and N. An odds ratio greater than 1 indicates that this motif is more likely to appear in the set P. To conduct statistical inference, one approach is to use large sample approximations to the sampling distribution of the log odds ratio. More precisely, the sample log odds ratio is: (2) and the standard error for the log odds ratio is approximately: (3) Then, the z-valueZ(m) = LOR(m)/SE(m), (4)follows a standard normal distribution. Finally, we can calculate the p-value to assess the statistical significance of each motif. Frequent motif miningGiven a set of phosphorylated peptides P and a user-specified support threshold s, the objective of frequent motif finding is to discover all motifs whose supports are no less than s. The support for a motif is the percentage of phosphorylated peptides in P that match this motif. In other words, a motif m is frequent if and only if its cell count c00 in Table 1 satisfies c00 ≥ |P|s with | • | denoting the size of a set. Note that a similar occurrence constraint is utilized in the Motif-X algorithm [2], which represents the minimal number of phosphorylated peptides needed to match the residue/position pair in its greedy search procedure. There are two fundamental differences between our support constraint and their occurrence constraint: The support constraint is applied to the entire motif rather a single residue/postion pair. The support constraint can be used to prune the search space besides preventing the generation of random artifacts. One may argue that such a support constraint will result in the loss of some less frequent motifs that are statistically significant. We will explain its rationale from two perspectives: First, since the motif describes the phosphorylation pattern, it should be applicable to many substrates; otherwise, such pattern might be random artifact due to the limited number of known phosphorylation peptides.

Output: pass


Input: Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: Qayyum, A. M. ∙ Wakil, W. ∙ Arif, J. M. . . . Infection of Helicoverpa armigera by endophytic Beauveria bassiana colonizing tomato plantsBiol. Control. 2015; 90:200-207CrossrefScopus (86)45. Tadele, S. ∙ Emana, G. Entomopathogenic effect of Beauveria bassiana (Bals. ) and Metarrhizium anisopliae (Metschn. ) on Tuta absoluta (Meyrick) (Lepidoptera: gelechiidae) larvae under laboratory and glasshouse conditions in EthiopiaJ. Plant Pathol. Microbiol. 2017; 8:411-41546. Hosseinzadeh, A. ∙ Aramideh, S. ∙ Ghassemi-Kahrizeh, A. Efficacy of bio-insecticides on Tuta absoluta (Meyrick) (Lep. : gelechiidae) in laboratory and field conditionsIGR J. 2019; 21:164-17047. Shahini, S. ∙ Berxolli, A. ∙ Kokojka, F. Effectiveness of bio-insecticides and mass trapping based on population fluctuations for controlling Tuta absoluta under greenhouse conditions in AlbaniaHeliyon. 2020; 6, e0575348. Gharekhani, G. H. ∙ Salek-Ebrahimi, H. Evaluating the Damage of (Meyrick) (Lepidoptera: Gelechiidae) on of Tomato under 2013 1-1049. Shiberu, T. ∙ Getu, E. Evaluation of biopesticides on integrated management of tomato leaf miner, Tuta absoluta (Meyrick) (Gelechiidae: Lepidoptera) on tomato crops in of central EthiopiaEntomol. , Ornithol. Herpetol. 2018; 7:1-8Crossref50. Buragohain, P. ∙ Saikia, D. K. ∙ Sotelo-Cardona, P. . . .

Output: corroborate


Input: Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: 52) and NHF (r = 0. 80) (Table S1). Furthermore, the positive THF in the demonstrates that the ocean surface largely influences the atmosphere, resulting in a higher AT. Arctic SIC varies most in the summer due to a persistently negative SIC anomaly (Fig. 4a). The summer months demonstrated a similar positive correlation with THF (r = 0. 61), RHF (r = 0. 86) and NHF (r = 0. 67) as the spring months (Table S1). The RHF has the strongest correlation. This could be due to increased incident solar radiation, more open ocean absorbing heat, and considerable reduction in sea ice 15. In summer, the THF anomaly is weakly positive, while the RHF anomaly is found to be strongly positive in the marginal seas of the (Fig. 4b, c). NHF is largely negative during the negative phase of SIC (Fig. 4d). The LH anomaly is negative because most of the heat is absorbed by the ocean (Fig. S2), and it is significantly correlated with the SIC (r = 0. 71, p <0. 01). During summer, the atmosphere loses heat to the ocean, causing increased ocean warming 14. Hence, atmospheric warming is less than that of the ocean. In early autumn, a weak positive ice anomaly is observed when the amount of sunlight decreases in the Arctic. However, from the analysis, autumn has the second-highest negative SIE trend (Table 1). The negative phase of SIC in autumn shows maximum sea ice loss in the region surrounding the (Fig. 5a). In contrast to the previous seasons, autumn shows a significant positive and negative correlation with the THF and RHF, respectively (Table S1). The RHF and THF anomalies show positive and negative anomalies, respectively (Fig. 5b, c). Similarly, the NHF does not correlate with the phenomenon that the atmosphere regulates sea ice, but strong positive LW and weak negative SW radiation were observed (Fig. S3). In autumn, the NHF pattern reverses from the previous summer, with positive regions becoming negative (Fig. 5d). The mechanism behind the pattern reversal is probably related to the fact that heat absorbed in the summer is released in the winter 14,43. This leads to maximum warming in the autumn and further delays in the subsequent winter when the sea ice freezes.

Output: contrast


Input: Claim: Bioinformatics can eliminate all genetic diseases in the future. [SEP] Text: Second, we can use very small support threshold in the mining process to avoid missing infrequent motifs. In the extreme case, setting s = 1/|P| will guarantee the completeness. Certainly, this may result in the report of many meaningless motifs. From this viewpoint, we can regard the support threshold as a parameter for controlling the trade-off between completeness and false positive. More importantly, the use of support constraint enables us to exploit a level-wise pruning strategy so as to reduce the search space. This idea has been widely used since the introduction of Apriori algorithm for association rule mining [4]. The application of this strategy to frequent motif finding is rather straightforward. For the sake of completeness, we will describe the mining procedure briefly. In this paper, we focus on the discovery of pattern-based phosphorylation motifs, e. g. , consensus sequences that consist of either conserved positions or wild positions (denoted by “. ”) that can match any animo acids. Each motif has a single phosphorylated residue, which is denoted with a underlined character (S, T or Y). We define the size of a motif as the number of conserved positions in that motif. We also call a motif of size k a k-motif. We define that one k-motif is the generalization of another motif if they have the same conserved residues at k positions. For instance, “D…S. . P” and “D. Y. S. . P” are 2-motif and 3-motif, respectively. And the first motif is a generalization of the second one. Furthermore, we use F k to denote the set of all frequent k-motifs and Z k to denote the set of all potential frequent k-motifs. To find all frequent motifs, we utilize the level-wise search strategy rooted from the Apriori algorithm [4]. More precisely, the search procedure has multiple iterations to obtain the set of all frequent motifs. In the first iteration, we scan P to count the the number of phosphorylated peptides that match each possible 1-motif. In the subsequent k th iterations, we perform the following two operations: 1.

Output: pass


Input: Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: Evaluation of bio-pesticides against the tomato leaf miner, Tuta absoluta Meyrick (Lepidoptera: gelechiidae) in IndiaHorticulturae. 2021; 7:1-15CrossrefScopus (11)51. Klieber, J. ∙ Reineke, A. The entomopathogenic Beauveria bassiana has epiphytic and endophytic activity against the tomato leaf miner Tuta absolutaJ. Appl. Entomol. 2016; 140:580-589CrossrefScopus (99)52. Ndereyimana, A. ∙ Nyalala, S. ∙ Murerwa, P. . . . Field efficacy of entomopathogens and plant extracts on Tuta absoluta Meyrick (Lepidoptera: gelechiidae) infesting tomato in RwandaCrop Protect. 2020; 134CrossrefScopus (8)53. Castillo, D. L. ∙ Sword, A. G. The endophytic fungal entomopathogens Beauveria bassiana and Purpureocillium lilacinum enhance the growth of cultivated cotton (Gossypium hirsutum) and negatively affect survival of the cotton bollworm (Helicoverpa zea)Biol. Control. 2015; 89:53-60CrossrefScopus (190)54. Jallow, A. F. M. ∙ Dahab, A. A. ∙ Albaho, S. M. . . . Efficacy of some biorational insecticides against Tuta absoluta (Meyrick) (Lepidoptera: gelechiidae) under laboratory and greenhouse conditions in KuwaitJ. Appl. Entomol. 2018; 1-955. Gonza´lez-Cabrera, J. ∙ Molla´, O. ∙ Monto´n, H. . . . Efficacy of Bacillus thuringiensis (Berliner) in controlling the tomato borer, Tuta absoluta (Meyrick) (Lepidoptera: gelechiidae)BioControl. 2011; 56:71-80CrossrefScopus (121)56. Russo, M. L. ∙ Scorsetti, A. C.

Output: corroborate


Input: Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: During the negative winter SIC phase, the composite map shows a weak negative SIC anomaly in the with a strong negative anomaly in eastern Greenland and the Sea region (Fig. 6a). The winter SIC primarily depends on the THF and NHF; therefore, increased ocean-atmosphere warming may delay ice formation. The THF anomaly shows a significant positive correlation with SIC (r = 0. 35, p <0. 1), which is coherent with the findings of spatial distribution (Fig. 6b). However, the RHF anomaly is positive, which is due to dark days (Fig. 6c). There is no correlation between winter SIC and RHF. The NHF anomaly is negative in the except in the Sea (Fig. 6d). The variations in the NHF are a cumulative response of the THF and RHF; thus, a decrease in RHF depicts lower NHF. As a result, the SIC and NHF have a lower positive correlation (r = 0. 26) than the THF. The correlation analysis revealed significant positive and negative correlations with LH (r = 0. 40, p <0. 01) and SH (r = -0. 26, p <0. 10), respectively (Table 1). In winter, the sea-ice-air interaction is largely determined by the LW radiation (Fig. S4). ConclusionsArctic sea ice melting has been a striking feature of abrupt climate change over the past few decades. has had a significant negative and positive trend in SIC and temperature (SST and T2M), respectively, with the highest variation in the summer. The long-term annual Arctic SIE record shows a 22% decline since 1979. Summer (52%) is the season with the greatest loss, followed by autumn (27%), spring (15%), and winter (12%). The study shows increased Arctic sea ice loss over the last four decades, linked to an increase in Arctic SST at its periphery. Ocean-ice-atmosphere interactions govern recent Arctic warming during most of the year. atmospheric layer from the surface to the upper troposphere warms year-round. The cold seasons (autumn and winter) record more warming than the warm seasons (spring and summer). The differential atmospheric warming and cooling are due to changes in Arctic sea ice cover, ice-albedo feedback, and the seasonal cycle of ocean-atmosphere heat fluxes.

Output: contrast


Input: Claim: Bioinformatics can eliminate all genetic diseases in the future. [SEP] Text: Only the frequent motifs from F k –1 are used to generate Z k since a k-motif will not be frequent if one of its (k – 1) generalizations is infrequent. Therefore, the search space for k-motifs is reduced. An example search tree is given in Fig. 1. Figure 1An example search tree for browsing candidates in a level-wise manner. We start with an empty root node and perform width-first search. Only the nodes of frequent motifs are expanded for further checking. Full size image 2. The set P is scanned to count the support of candidates in Z k. Less frequent motifs are deleted from Z k to generate F k. We stop the search when F k is empty and return F = ∪F k as the final result. Motif-All algorithmThe Motif-All algorithm takes the support threshold s and the significance threshold θ as input to find phosphorylation motifs from two sets: a set of phosphorylated peptides P and a set of unphosphorylated peptides N. As shown in Fig. 2, it consists of the following steps:Figure 2Overview of the Motif-All algorithm. In the first step, we find frequent motifs from P to reduce the number of candidate motifs. In the second step, we perform significance test to report all over-expressed frequent motifs to the user. Full size image 1. Finding the set of all frequent motifs F from P using the level-wise search method introduced in the previous subsection. 2. Scanning the set N to calculate the log odds ratio for each motif m ∈ F. Those motifs whose p-values are no greater than θ are returned to the user. Note that the statistical evaluation of motifs can be done in various ways. Thus, we can also use other measures in Motif-All by simply replacing the log odds ratio with the new significance measure in step 2. The choice of significance evaluation measure will not change the completeness property of our algorithm. ResultsDataTo test the performance of Motif-All, we use the PhosPhAt database 3. 0 of Arabidopsis phosphorylation sites [5, 6] to construct the set of phosphorylated peptides P. Note that we only utilize unambiguous site identifications in the construction process.

Output: pass


Input: Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: swirskii can interfere with other BCAs by inter- and intraguild predation (hyperpredation) (Buitenhuis et al. 2010; Messelink et al. 2011). In the field, A. swirskii is primarily used preventatively to avert pest establishment and for controlling light whitefly and thrips infestations (Messelink et al. 2010; Calvo et al. 2011). At high pest pressure, curative control by predatory mites is often insufficient and corrective applications with chemical pesticides or biopesticides may be required (Calvo et al. 2009; Medd and GreatRex 2014). Ideally, the products used for corrective treatments should have minimal impact on other BCAs present in the crop. The fungal entomopathogen Beauveria bassiana (Balsamo) Vuillemin (Hypocreales: Cordycipitaceae) exhibits a wide host range and several strains have been developed as commercial mycoinsecticides (de Faria and Wraight 2007; Zimmermann 2007). Commercial formulations of B. bassiana are used curatively to suppress larger outbreaks of arthropod pests (Wraight et al. 2000; Faria and Wraight 2001; Inglis et al. 2001; Jacobson et al. 2001; Shipp et al. 2003). As both A. swirskii and B. bassiana target whitefly and thrips, it is natural to consider them as potentially complementary intraguild BCAs: A. swirskii for preventative control and B. bassiana for curative control. Due to its wide host range, B. bassiana may have detrimental effects on beneficial arthropods but this will depend on pathogen strain and host species (Seiedy et al. 2015). For instance, B. bassiana has been found to be infectious to hymenopteran parasitoids (Ludwig and Oetting 2001; Shipp et al. 2003) and may potentially disrupt their biocontrol functions. Despite the wide host range of B. bassiana, pathogenicity has only been reported to relatively few species of mites (Chandler et al. 2000). Interactions between arthropods and entomopathogens are however complex (Agboton et al.

Output: corroborate


Input: Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: In summer, when the ice-albedo feedback is strong, the upper ocean absorbs heat from the atmosphere. However, the surface layer is cooler in the summer because ice melting and heat transfer consume more energy than heating the atmosphere aloft. In autumn, the air above the surface is cooler than the ocean surface, allowing energy transfer from the ocean via RHF and THF. Sea ice loss contributes to atmospheric warming, reinforcing heat in the system and delaying ice growth. Winter sea ice loss is lower than summer, but most atmospheric warming is observed in this season. Winter warming causes sea ice to thin and fracture, causing dynamic sea ice movement. It also weakens atmospheric stratification as the and nearby continents become warmer due to the thin sea ice cover and open water. As atmospheric stability deteriorates, vertical mixing of the atmosphere and Arctic warming become easier. During warm seasons, negative albedo increases heat absorption in the ocean, delaying sea ice formation in the following winters and increasing atmospheric warming. Sea ice formation in cold seasons is directly related to the previous season's ocean and atmospheric heating. Sea ice allows for year-round warming from the surface to the upper atmosphere. Thus, thermodynamic processes are essential to understanding sea ice variation, which drives and responds to Arctic warming. Our findings suggest that Arctic warming is stimulated by seasonal variations in sea ice and ocean-atmosphere heat fluxes. Further work will require a comprehensive analysis to quantify the seasonal contribution to atmospheric warming. MethodsThe study utilized the monthly satellite-derived sea ice extent (SIE) available from the Center (NSIDC) on a 25 km x 25 km grid from January 1979 to December 2020 in the (www. nsidc. org). These datasets are obtained from and Microwave/Imager (SSM/I), radiance by NASA team algorithms from a Nimbus-7 Microwave (SMMR) and (DMSP) 9,44. Monthly averaged reanalysis data of sea ice concentration (SIC), sea surface temperature (SST), air temperature at 2 m (T2M), and vertical levels from 1000 hPa to 200 hPa on 0. 25° x 0. 25° grid are derived from ERA5 ( forecasts/datasets/reanalysis-datasets/era-interim).

Output: contrast


Input: Claim: Bioinformatics can eliminate all genetic diseases in the future. [SEP] Text: The length of each extracted peptide is 21 with a measured phosphorylated residue in the 11th position. To generate the background data set N, we first extract all 21-mers with a phosphorylated residue in the center position from the TAIR7 protein database. Then, we remove all peptides already in P. The remaining peptides form the background data. One may worry that such background data generation procedure will disable the extraction of meaningful motifs since N contains many peptides that can be phosphorylated but are not identified so far. We like to point out that these potential phosphorylated peptides are overwhelmed by those truly non-phosphorylated peptides in N. Thus, this data generation does not change the characteristics of the background set N. Overall, we generate three groups of data for serine (denoted by PhAtS), threonline (denoted by PhAtT) and tyrosine (denoted by PhAtY), respectively. Their characteristics are the following: PhAtS: It contains 2734 foreground sequences and 982050 background sequences. PhAtT: It contains 415 foreground sequences and 550574 background sequences. PhAtY: It contains 80 foreground sequences and 304344 background sequences. Performance comparisonIn the experiments, we compare our Motif-All algorithm against the Motif-X algorithm [2] and the MoDL algorithm [3]. Note that we didn’t include other motif finding algorithms such as [8] in the comparison. This is because these algorithms are not designed for phosphorylation motif discovery and it has been shown that MoDL [3] is superior to these methods. The results of Motif-All, Motif-X and MoDL are listed in Fig. 3. Some important observations are summarized as follows. Figure 3Performance comparison using data generated from PhosPhAt database 3. 0. In Motif-All, we fix the p-value threshold to be 10-6 and report the results at the support threshold of 8% and 10%, respectively. Since it is not allowed to upload more than 10MB data to the Motif-X server, we set the “background” option of Motif-X to “IPI Arabidopsis Proteome” to mimic the background data in our local machine. As shown in [2], Motif-X is robust to the choice of background data.

Output: pass


Input: Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: 2013; Aqueel and Leather 2013) and the impact of sublethal effects is poorly understood (Pozzebon and Duso 2010; Seiedy et al. 2012; Shipp et al. 2012). The aim of this study was to establish the potential of the commercially available B. bassiana strain GHA to infect and kill A. swirskii under optimal conditions for the pathogen and to investigate the effects on specific population parameters when subjected to topical application of B. bassiana conidial suspensions and/or dry residues on leaf discs. Materials and methodsMite cultures Amblyseius swirskii were acquired from a commercial culture at BCP Certis (Kent, UK) and reared on 50 mm French dwarf bean, Phaseolus vulgaris L. leaf discs at 25 °C, 70 % RH and 16:8 L:D. Cattail pollen (Typha sp. ) was provided as a food source ad libitum. Fungal culturesLaboratory cultures of B. bassiana strain GHA were established from the commercial product BotaniGard® 22 WP () by spread-plating 100 µl of a 0. 0625 % suspension onto Agar (SDA) in 90 mm diameter Petri dishes. Cultures were incubated in darkness at 25 °C and 75 % RH for 14 days. Sub-cultures were prepared by spread-plating 100 µl conidial suspensions. Preparation of conidial suspensionsFollowing 14 days of incubation, the B. bassiana cultures were dried by removing the Petri dish lid and placing the cultures in 25 °C and 40 % RH for 18 h (Baxter 2008). Conidia were dislodged and collected by tapping the Petri dish containing the culture into a sterile glass funnel (100 mm diameter) leading to a sterile 50 ml glass bottle. The conidia collected from 10 plates were suspended in 5 ml sterile water with 0. 02 % Tween® 80, and vortex mixed for 2 min (Mascarin et al. 2013). Conidial suspensions were prepared from dried cultures to represent the conidia from the dry formulation of the commercial product. The concentration of conidia was assessed using a 0. 1 mm (Hawksley, UK). Conidial suspensions were diluted to 2.

Output: corroborate


Input: Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: 7, 627–637 (2014). Kumar, A. , Yadav, J. & Mohan, R. Global warming leading to alarming recession of the Arctic sea-ice cover: Insights from remote sensing observations and model reanalysis. Heliyon 6, e04355 (2020). Screen, J. A. & Francis, J. A. Contribution of sea-ice loss to Arctic amplification is regulated by decadal variability. Nat. Clim. Chang. 6, 856–860 (2016). Tollefson, J. How hot will Earth get by 2100? Nature 580, 443–445 (2020). Cohen, J. , Pfeiffer, K. & Francis, J. A. episodes linked with increased frequency of extreme winter weather in the United States. Nat. Commun. 9, (2018). Overland, J. E. & Wang, M. Large-scale atmospheric circulation changes are associated with the recent loss of Arctic sea ice. Tellus, Ser. A Dyn. Meteorol. Oceanogr. 62, 1–9 (2010). Wu, B. , Zhang, R. , Wang, B. & D’Arrigo, R. On the association between spring Arctic sea ice concentration and Chinese summer rainfall. Geophys. Res. Lett. 36, L09501 (2009). Boé, J. , Hall, A. & Qu, X. September sea-ice cover in the projected to vanish by 2100. Nat. Geosci. 2, 341–343 (2009). Ogi, M. & Wallace, J. M. Summer minimum Arctic sea ice extent and the associated summer atmospheric circulation. Geophys. Res. Lett. 34, L12705 (2007). Rigor, I. G. , Colony, R. L. & Martin, S. Variations in surface air temperature observations in the Arctic, 1979-97. Journal of Climate vol. 13 896–914 (2000). Pithan, F. & Mauritsen, T. Arctic amplification dominated by temperature feedbacks in contemporary climate models. Nat. Geosci. 7, 181–184 (2014). Alexeev, V. A. , Esau, I. , Polyakov, I. V. , Byam, S. J. & Sorokina, S.

Output: contrast


Input: Claim: Bioinformatics can eliminate all genetic diseases in the future. [SEP] Text: Therefore, it is acceptable to use such setting in the performance comparison. Since the occurrence threshold of Motif-X has a similar effect as the support constraint of Motif-All, we set this parameter to be the value that is equivalent to the support value of 8%, i. e. , it is specified as 219, 33 and 6 for and PhAtY, respectively. We download the Matlab codes of MoDL to perform motif discovery using its default configurations. The notation “[]” denotes a conserved position that match any letter contained in the bracket. Full size imageFirstly, our algorithm is able to find more statistically significant phosphorylation motifs than existing algorithms. This is because our method has a theoretical guarantee on the completeness of results under a given parameter setting. In particular, almost all reported motifs of Motif-X and MoDL are included in the motif set of Motif-All. There are two exceptions: one is the motif “T D” detected by Motif-X and another is the motif “K. . T” found by MoDL. After checking these two motifs carefully, we found that the p-value of “T D” is 1. 1 × 10–5 and the p-value of “K. . T” is 2. 4 × 10–3 according to our significance test. Obviously, both motifs are not statistically significant according to the p-value threshold of 10–6. Secondly, the increase of support threshold for Motif-All will generate motif set that is very similar to that of Motif-X and MoDL. This is clearly visible in PhAtT and PhAtY. To further check if this is true, we also perform motif finding on PhAtS at the support threshold of 15%. We obtain three motifs under this setting: “R. . S”, “S. S” and “S P”. This result set is almost identical to that of Motif-X and MoDL listed in Fig. 3. This means that Motif-All not only can find more useful motifs but also is capable of serving as a substitute of existing algorithms in a flexible manner. Finally, we can generate more motifs using Motif-X by lowering the occurrence threshold.

Output: pass


Input: Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: 5 × 107 and 1 × 108 conidia ml−1, representing the lowest and highest recommended rates, for whitefly and thrips respectively, according to the BotaniGard® 22 WP label. Six replicate conidial suspensions were prepared per treatment for each experiment. To estimate viability of the conidia, 10 µl of the conidial suspension was applied onto a thin layer of SDA on one microscope slide per replicate conidial suspension (n = 6) placing a cover slip on top (Bugeme et al. 2008). The slides were incubated in darkness at 25 °C, 75 % RH for 18 h. Germination rate was estimated by assessing 100 conidia in four different fields of view per slide (Liu et al. 2003). Viability was >95 % in all experiments. BioassaysFemale adults of A. swirskii, age 2–4 days, were exposed to B. bassiana by topical application, dry residue on P. vulgaris leaf discs or a combined exposure with topical application followed by dry residue exposure, inspired by the methods of Dennehy et al. (1993) and Pozzebon and Duso (2010) as described below. The trial arenas (leaf discs in 50 mm Petri dishes) were incubated at 25 °C, 90 % RH and 16:8 L:D to maximise the likelihood of fungal infection. Topical application of A. swirskii females was conducted by immersing 10 mites individually in 1 ml of B. bassiana conidial suspensions of 2. 5 × 107 or 1 × 108 conidia ml−1 in a Petri dish for 30 s. The mites were then placed on filter paper to drain off excess liquid prior to transfer onto leaf discs using a fine brush. This procedure was replicated six times, hence 60 mites were tested per treatment. Control mites were immersed in a 0. 02 % Tween® 80 solution. Pollen (Typha sp. ) was provided as a food source in all treatments throughout the experiments. Dry residues of B. bassiana were prepared by immersing 50 mm leaf discs in 10 ml conidial suspensions of 2. 5 × 107 or 1 × 108 conidia ml−1, and 0. 02 % Tween® 80 for the control, for 30 s and air drying them for 1 h.

Output: corroborate


Input: Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: Vertical structure of recent arctic warming from observed data and reanalysis products. Clim. Chang. 2011 1112 111, 215–239 (2011). Screen, J. A. , Deser, C. & Simmonds, I. Local and remote controls on observed Arctic warming. Geophys. Res. Lett. 39, 10709 (2012). Graversen, R. G. , Mauritsen, T. , Tjernström, M. , Källén, E. & Svensson, G. Vertical structure of recent Arctic warming. Nature 451, 53–56 (2008). Holland, M. M. & Bitz, C. M. Polar amplification of climate change in coupled models. Clim. Dyn. 2003 213 21, 221–232 (2003). NSIDC. Arctic sea ice decline stalls out at second lowest minimum. and Analysis (2020). Onarheim, I. H. , Eldevik, T. , Smedsrud, L. H. & Stroeve, J. C. Seasonal and regional manifestation of Arctic sea ice loss. J. Clim. 31, 4917–4932 (2018). Kumar, A. , Yadav, J. & Mohan, R. Spatio-temporal change and variability of Barents-Kara sea ice, in the Arctic: Ocean and atmospheric implications. Sci. Total Environ. 753, 142046 (2021). Serreze, M. C. , Barrett, A. P. & Cassano, J. J. Circulation and surface controls on the lower tropospheric air temperature field of the Arctic. J. Geophys. Res. Atmos. 116, 7104 (2011). Higgins, M. E. & Cassano, J. J. Impacts of reduced sea ice on winter Arctic atmospheric circulation, precipitation, and temperature. J. Geophys. Res. Atmos. 114, (2009). Kim, K. Y. et al. Mechanism of Amplification and Loss. Sci. Rep. 9, 1184 (2019). Deser, C. , Tomas, R. , Alexander, M. & Lawrence, D. to Loss in the Late Twenty-First Century. J. Clim. 23, 333–351 (2010). Boé, J.

Output: contrast


Input: Claim: Bioinformatics can eliminate all genetic diseases in the future. [SEP] Text: For instance, if the occurrence threshold is set to 20 on PhAtS, Motif-X will return 31 motifs. To test whether Motif-All can still find these motifs, we use an approximately equivalent setting of s = 1% to conduct the experiment. Totally, Motif-All detects 1153 significant motifs that include all 31 motifs reported by Motif-X. The detailed results are available at of support constraintTo test the effect of support threshold, we vary this parameter from 0. 03 to 0. 09 and plot the corresponding numbers of discovered motifs and the running time in Fig. 4 and Fig. 5, respectively. Clearly, the number of identified motifs will decrease when the support threshold increases. Moreover, the use of a smaller support threshold may generate too many motifs. Therefore, it is plausible to give priority to larger support thresholds in parameter assignment. Figure 4The effect of support constraint on the number of identified motifs. Note that the number of identified motifs may far exceed the number of phosphorylated peptides. This is because the number of potential phosphorylation motifs is 21(L–1) – 1, where L is the length of each peptide. Full size imageFigure 5The effect of support constraint on the running time. Our algorithm is implemented with Java and is tested on a ThinkPad T400 notebook computer with 2. 4GHz CPU and 3GB RAM. Full size imageThe running efficiency test in Fig. 5 shows that the increase of support threshold will lead to the decrease of running time as well. More importantly, Motif-All can finish the motif finding procedure within 30 seconds, which is faster than Motif-X and MoDL. This means Motif-All is capable of discovering motifs from large data sets very efficiently. DiscussionThe discovery of phosphorylation motifs is a computationally challenging problem. This paper tries to resolve it through the introduction of Motif-All algorithm. Here we discuss several related problems that may need to be further investigated. Problem formulation: One critical question is how to formulate the computational problem for phosphorylation motif discovery. Currently, it is casted either as a search problem or as an optimization problem. Motif-X falls into the first category while MoDL belongs to the second category. In this paper, we opt for the first category since such formulation guarantees that each identified motif is statistically significant.

Output: pass


Input: Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: The mites in the dry residue and combined exposure trials were allowed to forage on B. bassiana residues for four days (Bugeme et al. 2008), representing the persistence of conidia on the top leaves of a plant in the field (Inglis et al. 1993). Survivorship and fecundity were monitored once per day for 10 days, removing the eggs from the leaf discs each day. On day 1, 4, 7 and 10 post-treatment, eggs were collected from each leaf disc using a fine brush and placed onto untreated leaf discs. The offspring were monitored for six days recording egg viability, juvenile survival and sex ratio. Dead mites were transferred to Petri dishes with moistened filter paper to encourage sporulation. Death by mycosis was confirmed by sporulation or red colouration of cadavers. The identity of the fungal growth on cadavers was confirmed by microscopic investigation of lactophenol blue stained conidiophores and conidia at magnification ×400, according to Humber (1997). The effect of B. bassiana residues on juvenile A. swirskii was studied to simulate the impact on new founding populations of mites dispersing into a newly treated crop. Ten gravid A. swirskii were allowed to oviposit on dry residue of the conidial suspensions, or 0. 02 % Tween® 80 as a control, for 16 h. The eggs deposited were corrected to 15 per replicate leaf disc (n = 6) and reared to adulthood for six days on the dry residue, recording egg viability and juvenile survival. Six days was sufficient for all mites to have reached maturity and to have mated. The surviving adult females were transferred to clean leaf discs and monitored for a further 10 days for survivorship and fecundity. Data analysisSurvival rates were estimated by parametric survival analysis with the Weibull distribution, due to non-constant hazards, in the R 3. 1. 2 statistical package (R 2012), which generates z values as a result of Wald tests. Multiple comparisons were conducted using Tukey’s post-hoc in the multcomp package in R (Hothorn et al. 2008).

Output: corroborate


Input: Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: , Hall, A. & Qu, X. Current GCMs’ Feedback in the Arctic. J. Clim. 22, 4682–4695 (2009). Kay, J. E. & Gettelman, A. Cloud influence on and response to seasonal Arctic sea ice loss. J. Geophys. Res. Atmos. 114, 18204 (2009). Kapsch, M. -L. , Graversen, R. G. & Tjernström, M. Springtime atmospheric energy transport and the control of Arctic summer sea-ice extent. Nat. Clim. Chang. 2013 38 3, 744–748 (2013). Lique, C. & Steele, M. Seasonal to decadal variability of heat content: A model-based analysis and implications for autonomous observing systems. J. Geophys. Res. Ocean. 118, 1673–1695 (2013). Kwok, R. , Pang, S. S. & Kacimi, S. Sea ice drift in the Southern Ocean: Regional patterns, variability, and trends. Elementa 5, (2017). Cao, Y. , Bian, L. & Zhao, J. Impacts of Changes in on Arctic Warming. Atmos. Clim. Sci. 09, 84–99 (2019). Notz, D. & Marotzke, J. Observations reveal external driver for Arctic sea-ice retreat. Geophys. Res. Lett. 39, (2012). Stroeve, J. & Meier. , W. Trends and Climatologies from SMMR and SSM/I, June to September 2001. (2008). Kalnay, E. et al. 40-Project. Bull. Am. Met. Soc. vol. 77 (1996). Budillon, G. , Fusco, G. & Spezie, G. A study of surface heat fluxes in the (Antarctica). Antarct. Sci. 12, 243–254 (2000). Additional DeclarationsNo competing interests reported. Supplementary FilesSupplementaryInformation. docxFigureS1. pdfFigureS2. pdfFigureS3. pdfFigureS4.

Output: contrast


Input: Claim: Bioinformatics can eliminate all genetic diseases in the future. [SEP] Text: However, it may report too many motifs than necessary as the statistical significance may not necessarily agrees with the biological interpretation. In this regard, optimization-based formulation has the merit of generating a concise motif set. Therefore, a better problem formulation incorporating the biological knowledge of phosphorylation is still needed. Motif evaluation: In Motif-All, we use two measures for motif assessment: statistical significance and support. Though our initial motivation of introducing the support constraint was to reduce the search space, we later found that it is also of practical importance in motif evaluation. Without the help of support constraint, it is almost impossible to obtain a concise motif set using only the significance test. From this perspective, we strongly recommend the adoption of support as one standard measure for phosphorylation motif evaluation in future studies. Furthermore, we also need to develop new evaluation measures since the combination of significance threshold and support is still insufficient for separating true phosphorylation motifs from false ones in some cases. The general question of developing an effective evaluation measure is open and needs more investigations. Motif utilization: Experimental methods to identify phosphorylation sites are time consuming, labor intensive and expensive [9, 10]. The identified motifs can be used to predict potential phosphorylation sites before biological validation. For instance, the recent released Scan-X method [11] is one such representative, which is built on Motif-X. The capability of finding more significant motifs using Motif-All makes it possible to build more accurate classifiers for better prediction. ConclusionsWe introduced the Motif-All algorithm for finding phosphorylation motifs. Motif-All can identify all statistically significant motifs under a given parameter setting. Meanwhile, it is very fast such that it is able to find hundreds of meaningful motifs from millions of peptide sequences within one minute on a personal computer. Our experimental results show that it outperforms existing phosphorylation motif discovery algorithms. We have shown that Motif-All is able to find more phosphorylation motifs than existing algorithms. However, it is very expensive and difficult to perform biological validation. To measure the correctness of the identified motifs that are not reported by other algorithms, one alternative strategy is to perform permutation test so as to control the false positive rate. Unfortunately, the permutation test is a very time-consuming procedure since it needs to execute Motif-All many times.

Output: pass


Input: Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: Fecundity rates were analysed using generalized linear mixed models (GLMM) with binomial errors, which uses the logit link function, in the lme4 package in R, where the proportion is the total number of eggs on a given day in a replicate leaf disc to the total population (number of surviving female mites + number of eggs) in that leaf disc on that day. Treatment was set as the fixed effect and replication and time (days numerated 1–10) as random effects. GLMM analysis allows comparisons of change in fecundity among treatments over time. Trend lines were plotted using generalised additive models (GAM). Egg viability, juvenile survival and sex ratio of offspring was analysed by generalized linear models (GLM) with binomial errors, which uses the logit link function to transform the data. This generates z values as a result of Wald tests. Tukey’s post-hoc analysis was conducted to identify significant differences between treatments. ResultsSurvivorshipThe B. bassiana treatments had a significant effect on the survival rates of adult A. swirskii compared with the control treatment. On dry residue the suspension with 1 × 108 conidia ml−1 significantly reduced survival compared with the control treatment (z = 2. 97, p < 0. 01). There was no significant difference between this treatment and the 2. 5 × 107 conidia ml−1 treatment (z = 1. 77, p = 0. 18), nor between the latter and the control (z = 1. 76, p = 0. 18) (Fig. 1a). Fig. 1Survivorship of Amblyseius swirskii adults exposed to a dry residue, b topical treatment, c combined treatment, and d juveniles on dry residue of Beauveria bassiana conidial suspensions (n = 6). Different letters indicate significant difference in survival rate as analysed by the Weibull distribution and compared using Tukey’s post-hoc (p < 0. 05). The ‘+’ signs indicate censoring, i. e. mites that are missing or survived the observation periodFull size image Topical application of B. bassiana significantly reduced the survival rates of adult A. swirskii for both 2. 5 × 107 (z = 2. 92, p < 0. 01) and 1 × 108 conidia ml−1 (z = 3.

Output: corroborate


Input: Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: navigation Apply for support FAQ Be involved toggle overview Mission statement PAGES logos fellows Meeting minutes us Supporters and partners Supporters and partners organization Scientific partners -of involved Be involved mailing lists Apply to be on the SSC Write for and education Stakeholder engagement Stakeholder engagement Engaging with stakeholders guidelines Endorsed and affiliated Former initiatives and groups Data stewardship Data guidelines PAGES databases External databases webinars PAGES awardees PAGES awardees Scholarship recipients Early-Guest scientists -Menu layer 3 left side Menu layer 3 right side -to-decadal Variability and Long-term sea ice seasonal-to-decadal variability and long-term change >Access contents of this issue >Download this 10. 22498/pages. 25. 1. 14 large-scale loss of Arctic sea ice in recent decades is one of the most prominent indicators of the ongoing global climate change. This derives from three main reasons. First, climate change is amplified in the Arctic (“Arctic amplification”, e. g. Pithan et al. (2013)), so consequences of changes in the global-mean climate are more readily observed at high latitudes compared to middle and lower latitudes. Second, while many observables change gradually with global mean climate, Arctic sea ice is among those observables that might eventually cross a binary threshold from “existing” to “non-existing”, which amplifies the perception of the underlying gradual trend. And third, as a consequence, changes in “Arctic sea ice coverage” are easier to grasp and communicate to a general public than changes in more abstract metrics such as “global mean temperature”. The observed changes in Arctic sea ice are not only a clear local indicator of large-scale climate change; the ice loss also has a number of sometimes far-reaching worldwide consequences. These include physical phenomena such as the possible impact on mid-latitude weather system or the disruption of the oceanic uptake of CO2, but also societal consequences such as the opening of new shipping routes or the necessary changes in the livelihood of the Arctic indigenous population. The importance of sea ice loss both as an indicator and as an active player in the ongoing climate change has motivated some intense research into understanding the temporal evolution of sea ice on time scales from seasons to decades. In this contribution, I use a combined analysis of the observational record and of climate model simulations to explain and summarize some of these findings.

Output: contrast


Input: Claim: All biological processes can be modeled perfectly with bioinformatics. [SEP] Text: Furthermore, it is very fast and is able to finish the mining of large data sets within a reasonable time period. The rest of the paper is organized as follows: Section 2 presents the Motif-All algorithm. Section 3 shows the experimental results. Section 4 gives some discussions and Section 5 concludes the paper. MethodsThere are two critical issues in phosphorylation motif finding. The first is how to measure the over-expressiveness of each motif. The second is how to perform the search in an efficient manner. In the proposed Motif-All method, we use odds ratio to evaluate if one candidate motif is over-expressed. To search efficiently, we use the following strategies to improve the running efficiency of Motif-All. We impose a support constraint on each candidate motif. Here the support for a motif is defined as the percentage of phosphorylated peptides that match this motif. The notation of support is widely used in association rule mining [4]. One motif is said to be frequent if its support is no less than a given threshold. The aim of introducing support constraint is two-fold: on the one hand, it can filter out non-frequent motifs that may correspond to random artifacts; on the other hand, it makes it possible to generate and prune motifs in a level-wise manner so as to avoid brute-force search. We divide the mining process into two stages. In the first stage, we perform frequent motif finding using only the data set P since the number of phosphorylated peptides is much smaller than that of unphosphorylated ones. In the second stage, we collect support information using the data set N to calculate the statistical significance of those candidate motifs from the first stage. We will report all phosphorylation motifs whose p-values are no larger than a user-specified significance threshold. Odds ratio and statistical significance scoreThe odds of an event is the probability that this event occurs divided by the probability that it does not occur. The odds ratio is defined as the ratio of the odds of an event in one group to the odds in the complementary group [7]. In the context of phosphorylation motif discovery, the first group corresponds to the set of phosphorylated peptides P and the second group is the set of unphosphorylated peptides N. For a given motif m, we can construct a contingency table as shown in Table 1.

Output: pass


Input: Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: 70, p < 0. 001) compared with the control treatment. There was no significant difference (z = 1. 53, p = 0. 27) between the two B. bassiana treatments (Fig. 1b). Combined exposure to topical application and dry residue of B. bassiana significantly reduced the survival rates of adult A. swirskii for both 2. 5 × 107 (z = 3. 39, p < 0. 01) and 1 × 108 conidia ml−1 (z = 3. 68, p < 0. 001) compared with the control treatment. There was no significant difference between the two B. bassiana treatments (z = 0. 92, p = 0. 61) (Fig. 1c). Beauveria bassiana did not have a significant effect on survival rates of juvenile A. swirskii reared from egg to adult on dry residue and monitored for a further 10 days after final ecdysis (Fig. 1d). There was no treatment effect on egg viability and juvenile survival was similar in all treatments. The scale parameter was <1 in all experiments indicating that the risk of dying decreases with time (Crawley 2007). This suggests the time frame of observation (10 days) was sufficient to capture the majority of the mites dying as a result of the treatment. Mites with confirmed mycosis were observed in all B. bassiana treatments except with juveniles reared on the dry residue of the conidial suspension with 2. 5 × 107 conidia ml−1. Dead mites exhibited pink to dark red colouration after death followed by sporulation. No mycosis was observed in the control treatments. FecundityThe oviposition rate decreased with time in all treatments and the number of eggs deposited per surviving female per day decreased at a higher rate for A. swirskii treated with B. bassiana than control mites (Fig. 2a–d). Exposing adult A. swirskii to dry residue of B. bassiana did not have a significant effect on fecundity (Fig. 2a). Juvenile A. swirskii exposed to dry residues however exhibited a significant decrease in fecundity in the subsequent mature life stage for 1. 0 × 108 (z = 3. 90, p < 0. 001) and 2. 5 × 107 conidia ml−1 (z = 2.

Output: corroborate


Input: Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: (2016), the incomplete knowledge of May sea ice concentration as reflected by the differences between different satellite data sets can cause differences in forecast mean temperature of several degrees, and in forecast sea ice area of 2 million km2. The incomplete knowledge of initial conditions of the sea ice cover arises because the microwave signature that is usually used to assess ice concentration reacts sensitively to snow coverage and melt-pond formation on the ice, for example. Different algorithms compensate these uncertainties in different ways, causing substantial differences of the observed sea ice area. Because of positive feedbacks such as the ice-albedo feedback, these differences in initial conditions are substantially amplified during summer and contribute to the much lower forecast quality of real-world applications compared to perfect model studies. Incomplete knowledge of the state of the underlying ocean certainly also contributes to these uncertainties. Second, the forecast skill might be negatively affected by the initial shock and drift in the forecast runs in a real-world application. Both are absent in a perfect-model study, as its forecasts are usually started from a model state that is perfectly consistent with model physics. In contrast, the initial state for any simulation starting from a model state based on observations will usually be more or less inconsistent with model physics, possibly causing substantial drift that can quickly compensate for any added value from the assimilation of observations. Figure 1: (a) Evolution of modeled and observed Arctic sea ice area in May and July (1979–2016. (b) sea ice area in July as a function of observed Arctic sea ice area in May of the same year. (c) sea ice area in July as a function of observed Arctic sea ice area in May of the same year. Observations for all panels are based on the Arctic sea ice index (Fetterer et al. , 2002, updated 2016). Model simulations for all panels are based on the first ensemble member of the CMIP5 simulations of MPI-ESM-LR (Notz et al. , 2013). A third factor that might cause the systematically better forecast skill in idealised model studies relates to possible model errors in the simulation of the persistence of the Arctic sea ice cover. Take, for example, the relationship between Arctic sea ice area in May and Arctic sea ice area in July during the observational period 1979–2016 (Fig. 1a). The time series of these two months are highly correlated because of their underlying trend, both in the observations and in the model simulations.

Output: contrast


Input: Claim: Every fish species has been cataloged and understood. [SEP] Text: Skip to Content eLife home page your research Search by keyword or author Reset form Feature 10. 7554/eLife. 45133 Open access Copyright information annotations (there are currently 0 annotations on this page). Share 15,540 views 1,069 downloads 117 5133. Copy to clipboard Download BibTeX Download. RIS 6 figures, 2 tables and 3 additional files 1 Download asset Open asset Total preprints posted to bioRxiv over a 61 month period from November 2013 through November 2018. (a) The number of preprints (y-axis) at each month (x-axis), with each category depicted as a line in a different color. Inset: The overall number of preprints on bioRxiv in each month. (b) The number of preprints posted (y-axis) in each month (x-axis) by category. The category color key is provided below the figure. Figure 1—source data 1 The number of submissions per month to each bioRxiv category, plus running totals. Download elife-45133-fig1-data1-v2. csv Figure 1—source data 2 workbook demonstrating the formulas used to calculate the running totals in Figure 1—source data 1. Download elife-45133-fig1-data2-v2. xlsx Figure 1—source data 3 The number of submissions per month overall, plus running totals. Download elife-45133-fig1-data3-v2. csv Figure 1—source data 4 The number of full-length 2018. Download elife-45133-fig1-data4-v2. docx Figure 1—source data 5 A table of the top 15 authors with the most preprints on bioRxiv. Download elife-45133-fig1-data5-v2. docx Figure 1—source data 6 A list of every author, the number of preprints for which they are listed as an author, and the number of email addresses they are associated with. Download elife-45133-fig1-data6-v2. csv Figure 1—source data 7 A table of the top 25 institutions with the most authors listing them as their affiliation, and how many papers have been published by those authors. Download elife-45133-fig1-data7-v2.

Output: pass


Input: Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: swirskii when exposed to topical application and/or dry residues under laboratory conditions with slight to moderate virulence (according to the IOBC toxicity classes). The topical treatment was intended to simulate the effect on a mite population in the crop directly exposed to a B. bassiana application followed by dispersal to untreated leaves or new growth. The dry residue exposure simulated mites dispersing into a recently treated crop whereas the combined exposure simulated the effect on mites exposed directly to the spray application and remaining on the treated leaves. Exposure of adult A. swirskii to B. bassiana at both the highest and lowest recommended dose according to the BotaniGard® 22 WP label significantly reduced survivorship of A. swirskii compared with the control treatment, indicating a clear treatment effect. There was no significant difference between the two doses applied in terms of survivorship, which is consistent with the results of et al. (2011) who found no difference in mortality of Neoseiulus californicus and Phytoseiulus persimilis treated with three different doses of B. bassiana. The survival rates of A. swirskii exposed to topical treatments of B. bassiana strain GHA corresponds to the mortality rates of A. swirskii exposed to B. bassiana strain F, as reported by Seiedy et al. (2015). The authors found the virulence of B. bassiana to A. swirskii to be dependent on pathogen strain indicating the importance of studying compatibility with specific strains, particularly commercially available strains such as GHA. Beauveria bassiana has been found to be infectious to other phytoseiid mites under laboratory conditions. Castagnoli et al. (2005) reported slight mortality of the spider mite predator N. californicus when exposed to topical treatment of B. bassiana. However, mortality was assessed after three days, whereas fungal pathogenesis may require several days to kill its host (Inglis et al. 2001; Shah and Pell 2003). Nevertheless, a significantly higher mortality of B. bassiana-treated N. californicus compared with the control was reported, with similar survivorship as reported for A. swirskii in the present study. et al. (2011), on the other hand, reported that although N. californicus is susceptible to B.

Output: corroborate


Input: Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: However, the detrended time series are only significantly correlated in the model simulations (Fig. 1b), with the Pearson rank coefficient of the correlations indicating a chance of far less than 1 % that these time series are uncorrelated. In contrast, chances are above 30 % that there is no significant correlation between the detrended observed time series of sea ice area in May and the detrended observed time series of sea ice area in July (Fig. 1c). This suggests that at least an idealised study based on the particular model employed here (MPI-ESM-LR) will result in an unrealistically large potential forecast skill of seasonal predictions. We are currently examining whether this finding also holds for other models, and are in particular trying to investigate the underlying reasons for this different behaviour in the model compared to reality. Annual variability: The importance of negative feedbacks In addition to seasonal forecasts on time scales of a few months, also forecasts on time scales of a few years have made some headlines over the past decade. These headlines were usually related to claims that the Arctic would lose its remaining summer sea ice within just a few years. The underlying reasoning of such claims was often related to a discussion of a possible ’tipping point’ that is related to the ice-albedo feedback. Given the substantial loss of Arctic sea ice in the past few years, the ocean could potentially absorb enough heat to rapidly melt the remainder of the sea ice cover. However, our current understanding of the Arctic climate system strongly suggests that this reasoning is unrealistic. A first indication for this finding derived from model experiments in which all Arctic sea ice was synthetically removed from the at the onset of summer, thus maximising the possible ice-albedo feedback (Tietsche et al. , 2011). Despite such maximised feedback, the ice cover recovered in these experiments within just a few years. This is because on annual time scales, negative feedbacks dominate the evolution of the Arctic sea ice cover. Three negative feedbacks are particularly important: First, the open ocean very effectively releases its heat to the atmosphere during winter, causing a rapid loss of much of the heat that was accumulated in the ice-free water during summer. Second, the thin ice that forms during winter can grow much more rapidly than ice that survived the summer, because heat can more effectively be transported from the ocean to the atmosphere when the ice cover is thin (Bitz and Roe, 2004).

Output: contrast


Input: Claim: Every fish species has been cataloged and understood. [SEP] Text: docx Figure 1—source data 8 A list of every indexed institution, the number of authors associated with that institution, and the number of papers authored by those researchers. Download elife-45133-fig1-data8-v2. csv Figure 2 with 4 supplements Download asset Open asset The distribution of all recorded downloads of bioRxiv preprints. (a) The downloads recorded in each month, with each line representing a different year. The lines reflect the same totals as the height of the bars in Figure 2b. (b) A stacked bar plot of the downloads in each month. The height of each bar indicates the total downloads in that month. Each stacked bar shows the number of downloads in that month attributable to each category; the colors of the bars are described in the legend in Figure 1. Inset: A histogram showing the site-wide distribution of downloads per preprint, as of the end of November 2018. The median download count for a single preprint is 279, marked by the yellow dashed line. (c) The distribution of downloads per preprint, broken down by category. Each box illustrates that category’s first quartile, median, and third quartile (similar to a boxplot, but whiskers are omitted due to a long right tail in the distribution). The vertical dashed yellow line indicates the overall median downloads for all preprints. (d) Cumulative downloads over time of all preprints in each category. The top seven categories at the end of the plot (November 2018) are labeled using the same category color-coding as above. Figure 2—source data 1 A list of every preprint, its bioRxiv category, and its total downloads. Download elife-45133-fig2-data1-v2. csv Figure 2—source data 2 The number of downloads per month in each bioRxiv category, plus running totals. Download elife-45133-fig2-data2-v2. csv Figure 2—source data 3 workbook demonstrating the formulas used to calculate the running totals in Figure 2—source data 2. Download elife-45133-fig2-data3-v2. xlsx Figure 2—source data 4 The number of downloads per month overall, plus running totals. Download elife-45133-fig2-data4-v2.

Output: pass


Input: Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: bassiana, virulence is low (<30 %) and not different to the control treatment over a 12-day period. Several studies have found the spider mite predator P. persimilis to be susceptible to B. bassiana topical treatment and exposure to dry residues under laboratory conditions. The studies reported susceptibility with slight to moderate virulence to P. persimilis after observation periods of three days (Duso et al. 2008), six days (Pozzebon and Duso 2010) and 12 days (et al. 2011). and Oetting (2001) reported high virulence to P. persimilis exposed to topical treatment of B. bassiana with 100 % mortality, although time of death and sporulation was not mentioned. With the exception of the latter, these studies report mortality rates comparable to the effects of B. bassiana to A. swirskii presented here. Sublethal effects of an entomopathogen on non-target predators can be manifested by a change in fecundity, predation rates, longevity and fitness of subsequent offspring (Duso et al. 2008; Seiedy et al. 2012; Shipp et al. 2012). There was an overall trend of reduced fecundity in B. bassiana-treated A. swirskii in the present study, except for adult mites exposed to dry residue only, indicating an effect on the fitness of the mites. Pozzebon and Duso (2010) reported no effect on the fecundity of P. persimilis when exposed to dry residue and/or topical treatment of B. bassiana but a significant reduction in eggs deposited was discovered when the mites were exposed to a combination of topical treatment, dry residue and feeding on B. bassiana treated prey. Furthermore, et al. (2011) demonstrated that B. bassiana topical treatment had no negative effect on the fecundity of N. californicus and P. persimilis at three different concentrations. It is therefore reasonable to conclude that B. bassiana can reduce the fecundity of phytoseiid mites, including A. swirskii, but primarily when experiencing a combination of exposure routes with high conidial concentrations. Apart from egg viability, the effect of B. bassiana on the offspring of treated individuals have not previously been studied for phytoseiid mites.

Output: corroborate


Input: Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: Third, as ice forms later in the season, it will carry a thinner insolating snow cover as any snow fall occurring before ice formation simply falls into the open ocean (Notz, 2009). The effectiveness of these negative feedbacks on an annual time scale is not only apparent in our model simulations; the observed time series of Arctic summer sea ice also carries a clear signature of such mechanisms. A year with a strong drop in ice coverage during September is usually followed by an increase in September ice coverage in the following year. More formally, the time series shows significant negative autocorrelation (Notz and Marotzke, 2012). If indeed the ice albedo feedback was as effective on annual time scales as implied by statements supporting the entire loss of Arctic summer sea ice within this decade, one would certainly expect that any year with a strong drop in ice coverage would be followed by a year with yet another drop. This is found neither in the observational record, nor in model simulation. This underpins the dominance of negative feedbacks, which stabilize the Arctic ice cover and prevent a possible “tipping point”. Decadal variability: The importance of internal variability Figure 2: (a) Evolution of modeled and observed Arctic sea ice area in September (1850–2016). (b) Evolution of ten-year linear trends of Arctic sea ice area, plotted at the end point of the ten year averaging period. (c) Evolution of thirty-year linear trends of Arctic sea ice area, plotted at the end point of the thirty year averaging period. (d) Evolution of modeled and observed Arctic sea ice area in September (1850–2100) as a function of cumulative anthropogenic CO2 emissions. Observations for all panels are based on the Arctic sea ice index (Fetterer et al. , 2002, updated 2016). Model simulations for all panels are based on the 100 member ensemble of MPI-ESM-LR. Internal climate variability not only governs a substantial part of the sea ice evolution on seasonal-to-interannual time scales as discussed above, but also affects the longer term trends of sea ice (Swart et al. , 2015; Notz, 2015). To exemplify this, large ensembles of simulations of coupled Models are particularly helpful (Swart et al. , 2015).

Output: contrast


Input: Claim: Every fish species has been cataloged and understood. [SEP] Text: csv Figure 2—figure supplement 1 Download asset Open asset The distribution of downloads that preprints accrue in their first months on bioRxiv. Figure 2—figure supplement 1—source data 1 Monthly download counts for each bioRxiv preprint for each of its first 12 months. Download elife-45133-fig2-figsupp1-data1-v2. csv Figure 2—figure supplement 2 Download asset Open asset The proportion of downloads that preprints accrue in their first months on bioRxiv. Figure 2—figure supplement 3 Download asset Open asset Multiple perspectives on per-preprint download statistics. Figure 2—figure supplement 3—source data 1 The download counts for each bioRxiv preprint in its first month online. Download elife-45133-fig2-figsupp3-data1-v2. csv Figure 2—figure supplement 3—source data 2 Maximum monthly download count for each bioRxiv preprint. Download elife-45133-fig2-figsupp3-data2-v2. csv Figure 2—figure supplement 3—source data 3 A list of each bioRxiv preprint and how many downloads it received in 2018. Download elife-45133-fig2-figsupp3-data3-v2. csv Figure 2—figure supplement 4 Download asset Open asset Total downloads per preprint, segmented by the year in which each preprint was posted. Figure 2—figure supplement 4—source data 1 A list of each bioRxiv preprint and how many downloads it received in each year it was online. Download elife-45133-fig2-figsupp4-data1-v2. csv Figure 3 with 1 supplement Download asset Open asset Characteristics of the bioRxiv preprints published in journals, across the 27 subject collections. (a) The proportion of preprints that have been published (y-axis), broken down by the month in which the preprint was first posted (x-axis). (b) The proportion of preprints in each category that have been published elsewhere. The dashed line marks the overall proportion of bioRxiv preprints that have been published and is at the same position as the dashed line in panel 3a. (c) The number of preprints in each category that have been published in a journal.

Output: pass


Input: Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: bassiana conidia on leaf surfaces can explain the low susceptibility of juvenile A. swirskii to this pathogen. Viability of B. bassiana conidia decreases gradually on leaves and persistence on top leaves has been reported to be four days (Inglis et al. 1993). As A. swirskii eggs, which were not affected by B. bassiana dry residue, hatch 1–2 days post-oviposition and the nymphs moult to the next instar every 1–2 days under optimal conditions (Lee and Gillespie 2011; Midthassel et al. 2013) the juvenile stages are able to evade pathogen invasion during the first few days post-application and therefore survive until the conidial viability has been reduced. The effect of B. bassiana residue on juvenile phytoseiids has not previously been studied and these results offer the first insight into the effect of B. bassiana on the population-founding individuals in a treated crop. Field studies of B. bassiana applications with concomitant inoculations of phytoseiid mites have largely concluded that there is no detrimental effect on the phytoseiids and deemed these BCAs as compatible (Jacobson et al. 2001; Chandler et al. 2005; et al. 2011). Compatibility however does not warrant an additive or synergistic contribution to pest control. Jacobson et al. (2001) found that combining B. bassiana with Neoseiulus cucumeris did not improve nor disrupt thrips control in a cucumber crop and B. bassiana treatments in conjunction with N. californicus or P. persimilis did not improve nor disrupt Tetranychus urticae Koch (Acari: Tetranychidae) control in roses (et al. 2011). Enhanced T. urticae control has been reported on tomato with concomitant use of B. bassiana and P. persimilis compared with either one on their own (Chandler et al. 2005) suggesting that improved control by combined treatments may be crop-specific or dependent on pest level and age distribution. Although concomitant pest control with A. swirskii and B. bassiana may not be additive or synergistic, these two BCAs may complement each other in an IPM programme: A. swirskii for preventative control and B. bassiana as a curative treatment.

Output: corroborate


Input: Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: At the Max-Planck-Institute for Meteorolgy, we have recently finished a 100 member large ensemble of simulations with our -ESM-LR for the historical period, and for two possible future emission scenarios, RCP2. 6 and RCP4. 5. In comparing the simulated Area sea ice area during September with the observational record 1979–2016, we find that the observed sea ice area is at the upper edge of the ensemble spread during the earlier years of the record, and approaches the mean of the ensemble in the more recent past (Fig. 2a). Ten-year long trends within the observational record have consistently been negative with values ranging from a mean loss of 23 000 km2 per year during the period 1990–1999 to a mean loss of 180 000 km2 during the period 2003–2012 (black line in Fig. 2b). This latter period includes the two record minima that have been observed in 2007 and 2012. For the most recent period 2007-2016, the average ice loss has been around 50 000 km2 per year. All these numbers are well within the range simulated by individual ensemble members of MPI-ESM-LR, which show over ten year-long periods a sea ice evolution ranging from a mean loss of around 200 000 km2 per year to a mean gain of around 100 000 km2 per year (shaded range in Fig. 2b). In particular, it is noteworthy that the mean ten-year long trends for the first 20 years of the satellite record and for the most recent past are very close to the mean trend of the 100 member ensemble, including a slowdown of the ice loss during the 1990s and an accelaration during the early 2000s. This agreement during substantial periods of the record suggests that the rather sudden drop in observed sea ice area that occurred in the year 2007 has been an extreme event compatible with internal climate variability and therefore cannot directly be compared to the ensemble mean across several models or to the ensemble mean across multiple simulations with one model. If this characterisation of this sea ice loss as an extreme event was correct, any realistic climate model should on average simulate a slower ice loss than has been observed (see also Notz (2015) for a detailed discussion). Regarding the future evolution of sea ice, the model simulations with MPI-ESM-LR suggest a similar range of possible ten year trends than over the past few decades.

Output: contrast


Input: Claim: Every fish species has been cataloged and understood. [SEP] Text: Figure 3—source data 1 The number of preprints posted in each month, plus the count and proportion of those later published. Download elife-45133-fig3-data1-v2. csv Figure 3—source data 2 The number of preprints posted in each category, plus the count and proportion of those published. Download elife-45133-fig3-data2-v2. csv Figure 3—figure supplement 1 Download asset Open asset Observed annual publication rates and estimated range for actual publication rates. Figure 3—figure supplement 1—source data 1 Results of manual publication verification for a sample of bioRxiv preprints. Download elife-45133-fig3-figsupp1-data1-v2. xlsx Figure 4 Download asset Open asset A stacked bar graph showing the 30 journals that have published the most bioRxiv preprints. The bars indicate the number of preprints published by each journal, broken down by the bioRxiv categories to which the preprints were originally posted. Figure 4—source data 1 The number of preprints published in each category by the 30 most prolific publishers of preprints. Download elife-45133-fig4-data1-v2. csv Figure 4—source data 2 A table showing the proportion of published papers that were previously bioRxiv preprints, for the 30 journals that published the most bioRxiv preprints. Download elife-45133-fig4-data2-v2. docx Figure 4—source data 3 Year-level data of the proportion of published papers that were previously bioRxiv preprints, for the 30 journals that published the most bioRxiv preprints. Download elife-45133-fig4-data3-v2. xlsx Figure 5 Download asset Open asset A modified box plot (without whiskers) illustrating the median downloads of all bioRxiv preprints published in a journal. Each box illustrates the journal’s first quartile, median, and third quartile, as in Figure 2c. Colors correspond to journal access policy as described in the legend. Inset: A scatterplot in which each point represents an academic journal, showing the relationship between median downloads of the bioRxiv preprints published in the journal (x-axis) against its 2017 journal impact factor (y-axis).

Output: pass


Input: Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: When pest populations build up beyond the level where reasonable control can be expected from predatory mites, corrective applications with B. bassiana can be conducted followed by re-introduction of A. swirskii if needed. The effect of B. bassiana on other arthropod BCAs in the IPM programme must still be considered, and the potential detrimental effect of other components, such as fungicides, on B. bassiana must be taken into account. IPM programmes are complex and inconstant and there is much further work to be conducted to fully understand and optimise these control strategies. Based on the results from the current study the susceptibility of A. swirskii as a physiological host for B. bassiana GHA is confirmed with slight to moderate virulence depending on exposure type. Despite the sublethal effects of B. bassiana on the fecundity of A. swirskii, these two BCAs have good potential for concomitant use in the field due to the following points: (1) no significant effect of dry residue on juvenile survival; (2) no significant effect of dry residue on fecundity; (3) no detrimental effect on the offspring of infected A. swirskii with respect to egg hatching, juvenile survival and sex ratio; and (4) the mortality in field conditions are generally expected to be lower than in the laboratory. Further work should focus on field compatibility of these two BCAs.

Output: corroborate


Input: Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: Hence, in extreme cases and providing the hypothesis that the level of internal climate variability simulated is correct, the sea ice cover might in the future potentially once again lose ice as fast as during the first decade of this century, or, in contrast, gain an average of 100 000 km2 per year for a decade despite the ongoing global warming (see also Swart et al. , 2015). In order to more confidently predict the near-term evolution of Arctic sea ice, the underlying reasons for internal variability must be understood better. A number of recent studies point in particular to the impact of oceanic heat transport into the Arctic for driving low-frequency variability of the ice cover, including a possible contribution to the recent acceleration of sea ice loss (e. g. , Årthun et al. , 2012). These studies emphasize that a scenario with a much slower sea ice loss for the next decade is plausible if oceanic heat transport were to weaken (Zhang, 2015; Yeager et al. , 2015). Such weakening of the oceanic heat transport would not only affect the sea ice cover itself, but also its future predictability on seasonal time scales (Germe et al. , 2014). This then directly links the challenge of decadal forecasting of sea ice to that of its seasonal forecasting as described in the previous section. Long-term changes: The importance of the external forcing On longer time scales, internal variability also remains a substantial contributor to the evolution of Arctic sea ice area. The 100 member simulations suggest a possible spread in observed September sea ice area of around 2 million km2 for any given year (Fig. 2a). In terms of trends, even long-term trends over 30 years show substantial variability (Fig. 2c). For example, the model suggests that over the past 30 years, sea ice area in the Arctic could have remained roughly constant or could have decreased roughly as quickly as observed. This large spread in 30-year long trends again suggests that using these trends as metrics for the purpose of model evaluation can be misleading (Notz, 2015), in particular if the observed evolution of Arctic sea ice corresponds to a possible extreme event. Despite the large impact of internal variability, the dominant role of external forcing in the observed evolution of Arctic sea ice is clear (Notz and Marotzke, 2012).

Output: contrast


Input: Claim: Every fish species has been cataloged and understood. [SEP] Text: The size of each point is scaled to reflect the total number of bioRxiv preprints published by that journal. The regression line in this plot was calculated using the ‘lm’ function in the R ‘stats’ package, but all reported statistics use the Kendall rank correlation coefficient, which does not make as many assumptions about normality or homoscedasticity. Figure 5—source data 1 A list of every preprint with its total download count and the journal in which it was published, if any. Download elife-45133-fig5-data1-v2. csv Figure 5—source data 2 Journal impact factor and access status of the 30 journals that have published the most preprints. Download elife-45133-fig5-data2-v2. csv Figure 6 Download asset Open asset The interval between the date a preprint is posted to bioRxiv and the date it is first published elsewhere. (a) A histogram showing the distribution of publication intervals. The x-axis indicates the time between preprint posting and journal publication; the y-axis indicates how many preprints fall within the limits of each bin. The yellow line indicates the median; the same data is also visualized using a boxplot above the histogram. (b) The publication intervals of preprints, broken down by the journal in which each appeared. The journals in this list are the 30 journals that have published the most total bioRxiv preprints; the plot for each journal indicates the density distribution of the preprints published by that journal, excluding any papers that were posted to bioRxiv after publication. Portions of the distributions beyond 1,000 days are not displayed. Figure 6—source data 1 A list of every published preprint, the year it was first posted, the date it was published, and the interval between posting and publication, in days. Download elife-45133-fig6-data1-v2. csv Figure 6—source data 2 A list of every preprint published in the 30 journals displayed in the figure, the journal in which it was published, and the interval between posting and publication, in days. Download elife-45133-fig6-data2-v2. csv Figure 6—source data 3 The results of Dunn’s test, a pairwise comparison of the median publication interval of each journal in the figure.

Output: pass


Input: Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: Extracellular enzyme activity of entomopathogenic fungi, Beauveria bassiana and Metarhizium anisopliae and their pathogenicity potential as a bio-control agent against whitefly pests, Bemisia tabaci and Trialeurodes vaporariorum (Hemiptera: Aleyrodidae) note Open access Published: 26 March 2022 Extracellular enzyme activity of entomopathogenic fungi, Beauveria bassiana and Metarhizium anisopliae and their pathogenicity potential as a bio-control agent against whitefly pests, Bemisia tabaci and Trialeurodes vaporariorum (Hemiptera: Aleyrodidae) Amha Gebremariam1, Yonas Chekol1 & Fassil Assefa1 BMC 3. 41), lipase (EI = 4. 45), and protease activities (EI = 5. 44) respectively. The pathogenicity study of isolates on whitefly nymphs and adults indicated significant variation (P < 0. 05) with mortality ranging from 71. 67 to 98. 33% and 60 to 100% against Bemisia tabaci and Trialeurodes vaporariorum nymphs respectively. The mortality of adults was between 58 and 94. 27% against B. tabaci and 59. 03 to 95. 37% against T. vaporariorum. The result also showed that AAUMB-29, -77, and AAUDM-43 were the most virulent with LC50 values of 2. 7 × 104, 5. 3 × 104, and 5. 4 × 104 conidia/ml against nymphs of B. tabaci, and with LC50 values 6. 8 × 104, 8. 2 × 104, and 7. 2 × 104 conidia/ml against nymphs of T. vaporariorum, respectively. The B. bassiana AAUMB-29, B. bassiana -77, and M. anisopliae AAUDM-43 induced the highest whitefly mortality than other isolates. These isolates can be recommended for further tests under field conditions to fully realize their potential as effective biocontrol agents against whitefly pests in tomato.

Output: corroborate