Input: Task: corroborate the claim using the given text. Claim: Some wearable devices use heart rate and movement data to estimate sleep stages. [SEP] Text: ” Instead they adopted a newer actigraphy monitor that reports additional physiological data to more accurately determine whether a person is awake or asleep: The EmbracePlus smartwatch tracks a person’s movement, skin conductance, body temperature and heart rate, among other metrics, and provides the researchers with access to the device’s raw data. They also plan to combine the actigraphy with another measure, such as EEG from the Dreem headband, to create an even fuller portrait of a person’s sleep behavior — and more akin to the data obtained in a traditional sleep study. (Dinstein has also received funding from the for his research. ) Dinstein and his colleagues are piloting this pairing of the EmbracePlus and the Dreem headband on non-autistic adults and later plan to use the devices in tandem to track sleep in autistic adults and children. The eventual goal, Dinstein says, is to run an intervention study to see whether improving autistic people’s sleep eases other autism-linked traits. “The really interesting question is to what degree the change in sleep” will affect other behavioral domains, he says. Making waves Even in short studies on sleep, having participants wear EEG headbands was a challenge, says Dina Katabi, director of the Center for at the of Technology. “Without even knowing about it, they just rip it off their head while they sleep. ” But what if a sleep tracker didn’t even need to touch the body of the person it tracks? The Emerald, which mounts on a wall and resembles a wireless internet router, was born in Katabi’s lab to test that question. The device sends radio waves into the surrounding space and tracks how a person’s movements distort the waves’ return, not unlike how sonar maps what lies below a ship. Unlike polysomnography, the Emerald does not create a real-time readout of someone’s brain activity, heart rate, motion or breathing. Instead, the device feeds its data through an artificial intelligence processing program trained on polysomnography data. The lab has validated the device in neurotypical people, and it can measure disease severity and medication responses in people with Parkinson’s disease, according to a pair of 2022 papers from Katabi’s lab that used the Emerald to track gait and nighttime breathing. [SEP] Justify:

Output: The text corroborates the claim by detailing the EmbracePlus smartwatch, which tracks movement and heart rate, among other physiological data, to determine whether a person is awake or asleep. This aligns with the claim that wearable devices utilize such data to estimate sleep stages.


Input: Task: contrast the claim using the given text. Claim: Most modified trees can be planted anywhere without affecting native flora. [SEP] Text: In roadside verges a number of threatened fungi occur that presumably have disappeared from forests with the same tree species. In 25 plots of the Hypochaeris radicata-Quercus type with trees of three different ages successional series were studied. The number of ectomycorrhizal species increases with the tree age. This is in contrast to data from forests in the literature, where the species number decreases after an initial increase. The differences in soil and the management practices in roadside verges may explain this discrepancy. Eutrophication and litter accumulation cause a decrease in the species number, resulting in low species numbers in eutrophicated places and/or in thick litter layers, even under old trees. A new, preliminary classification of ectomycorrhizal fungi regarding their respective optima during the sucession of the site, based on the data from roadside verges is presented. In a homogeneous roadside verge with 100 year old along a canal the effects of various management treatments were studied. During the years 1987-'91, 5 different treatments were applied: a. mowing without removal of the hay, b. mowing with removal of the hay, c. nonrecurrent removal of sods in combination with mowing without removal of the hay, d. N-fertilization during the first 3 years in combination with mowing without removal of the hay, e. no treatment. Treatment "a" served as control. Each treatment was carried out sixfold in plots of 50x4. 5 m 2. The phanerogam vegetation, some soil properties and ectomycorrhizal samples from treatments b, c and d were analyzed as well. Removal of sods caused an immediate decrease of sporocarps of mycorrhizal fungi but after 3 years the species numbers had largely recovered. However, there was some shift in species composition. After fertilization a strong decrease of ectomycorrhizal fungi occurred. The saprotrophs were significantly afflicted by removal of sods. Fertilization resulted in a decline in saprotroph species numbers but the production did not change significantly. Treatments b and e showed no significant differences with the control. The soil chemistry was not influenced by the treatments except for higher concentrations of N in fertilized plots. In eight open beech plots, viz. [SEP] Justify:

Output: The text contrasts the claim by providing substantial evidence that the introduction of certain tree species in non-native environments, such as roadside verges, can negatively impact local flora and fauna, including ectomycorrhizal fungi. It highlights that changes in management practices and environmental conditions, like eutrophication and litter accumulation, lead to a decline in species diversity and alter the species composition of ectomycorrhizal fungi, indicating that modified trees can indeed affect native ecosystems.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: The first arrival time decreases with increasing field strength, however, the poor throughput for weak fields is mostly due to the slow arrival of the bulk of the bacteria, while some bacteria still cross the channel on an almost direct path. The fraction of arrivals, by contrast, shows the non-monotonic behavior indicated by our experimental results. Results for different channels (grey lines in Figure 6 c) are qualitatively similar. Quantitatively, the arrival fraction ϕtot shows considerable dependence on the channel geometry, with maximal arrival fractions between 50 and 100 % and maximal throughput in the range between a few and a few tens of µT. By contrast, the first-arrival time t f seems not to be affected by channel geometry (Figure 6 c). Fig. 6. Effect of magnetic field strength on simulated swimming through obstacle channels:(a) Motion of bacteria in a channel for different values of the field strength B (heat maps of the densities of arriving and non-arriving bacteria, blue and red, respectively, as in Figure 5) (b) Fraction ϕ of simulated bacteria that have arrived up to time t for different B. Circles mark the first-arrival times tf; crosses with thin lines mark total arrival fractions ϕtot. The curve for B = 500 µT remains at 0. (c) First-arrival times tf (top) and total arrival fraction ϕtot (bottom) as a function of the field strength B. With stronger fields, tf converges to the first-arrival time of a persistent swimmer in an empty channel (dashed grey line). ϕtot shows a peak at an intermediate (optimal) field strength, which can be explained by the interplay of two opposing effects that arise with stronger fields: effectively faster motion in the direction of the field and higher susceptibility to trapping. Blue data points show the results for the channel shown in a), gray lines show results in other channels. 2. 4 Escape from trapsOur simulations indicate that trapping of bacteria in the corners between overlapping obstacle pillars plays a crucial role for the non-monotonic dependence of the bacterial throughput on the magnetic field. We therefore investigated the escape from traps in more detail by simulating a large number of trap configurations with different geometric features. [SEP] Justify:

Output: The text does not address the claim about sea turtles and their navigation using the Earth's magnetic field. Instead, it discusses the behavior of bacteria in relation to magnetic field strength and their arrival times in a simulated environment. Therefore, it is irrelevant to the claim about sea turtles.


Input: Task: corroborate the claim using the given text. Claim: Some wearable devices use heart rate and movement data to estimate sleep stages. [SEP] Text: Conclusions: This study supports utilizing HRV alongside an accelerometer as a means for distinguishing sleep from wake and for identifying sleep stages. method was able to detect light sleep and slow wave sleep with no statistically significant difference to PSG. Firstbeat underestimated REM sleep and overestimated wake time. This study suggests that Firstbeat is a feasible method with sufficient validity to measure nocturnal sleep stage variation. 2021;9(2):e24704doi:10. 2196/24704Keywordselectroencephalogram; actigraphy; polysomnography; sleep; heart rate; rapid eye movements IntroductionSleep stages alternate throughout the typical nighttime sleep period. After the initial sleep onset, nonrapid eye movement (NREM) sleep stages 1 (N1), 2 (N2), and 3 (N3) emerge alongside rapid eye movement (REM) sleep [1]. Together, both NREM and REM sleep stages form sleep cycles, which, in healthy adults, rotate approximately four or five times over the course of a single night [2]. This alteration between stages correlates with changes in physiological measures such as muscle tonus [3,4], blood pressure [5-7], temperature regulation [8,9], as well as heart rate and heart rate variability (HRV) [10,11]. More specifically, NREM sleep stages are related to stability in the cardiovascular system and stronger parasympathetic cardiac modulation. This, in turn, is reflected in REM sleep so that the heart rate increases and becomes less stable [5,7]. Within NREM sleep stages, the differences between deep sleep (N3, or slow wave sleep [SWS]) and lighter sleep stages (N1 and N2) also have some physiological differences, but these are less pronounced than those between REM and NREM [12]. Specifically, the deeper the sleep, the stronger the parasympathetic cardiac modulation (ie, SWS is associated with a lower heart rate compared to N2 and N1) [13,14]. Polysomnography (PSG) is considered the gold-standard means for measuring sleep stages, as the combination of electromyography (EMG) and electroencephalograph (EEG) is, by definition, the only way to distinguish between the different sleep stages [1]. [SEP] Justify:

Output: The text corroborates the claim by discussing how heart rate variability (HRV) and accelerometers can be utilized to distinguish between sleep and wake states, as well as to identify different sleep stages. It mentions that ‘this study supports utilizing HRV alongside an accelerometer’ for measuring various sleep parameters, which aligns with the idea that wearable devices can estimate sleep stages based on heart rate data.


Input: Task: contrast the claim using the given text. Claim: Most modified trees can be planted anywhere without affecting native flora. [SEP] Text: The classification of the oak and beech plots together on the basis of the green plants is largely analogous to the classifications of oak and beech plots separately. For the saprotrophic fungi, values, thickness of the organic layer and the openness of the plots are determining factors in the classification of the plots. However, for the classification of the plots using the ectomycorrhizal fungi the tree species is the most important parameter. Within the oak and beech group, the classification resembles the classifications of oak and beech plots separately. In plots with eutraphent vegetation nonhost-specific ectomycorrhizal fungi dominate. In such plots the ectomycorrhizal mycoflora of oak and beech plots is more or less similar. A comparison of the fungal communities of roadside verges with forest communities of the same tree species was made. Comparison with Dutch oak forest communities revealed that 42 ectomycorrhizal species were found to be differential for roadside verges and 39 for forests. 16 species were indifferent. Among the saprophytes, 24 species were differential for roadside verges, at least 62 for forests and 24 were indifferent. Only the Dicrano-Quercetum showed resemblance with some types of oak plots. The main difference with the other types of oak forest is the larger number of ectomycorrhizal fungi in roadside verges. Regarding the saprotrophic fungi, roadside verges differ profoundly from forests. Terrestrial raw humus inhabiting and lignicolous fungi are mostly restricted to forests, whereas typical grassland fungi were mostly found in roadside plots. Dutch oak forest communities showed more similarity with the roadside verge communities than those from other parts of Europe. Generally, communities with little or no organic layers on nutrient-poor soils have a large proportion of ectomycorrhizal species in common with the analogue roadside communities. A classification is presented for ectomycorrhizal roadside fungi based on their assumed restricted occurrence in the roadside habitat. In roadside verges a number of threatened fungi occur that presumably have disappeared from forests with the same tree species. In 25 plots of the Hypochaeris radicata-Quercus type with trees of three different ages successional series were studied. The number of ectomycorrhizal species increases with the tree age. This is in contrast to data from forests in the literature, where the species number decreases after an initial increase. [SEP] Justify:

Output: The text contrasts the claim by discussing the ecological impacts of different fungi in various habitats related to specific tree species. It emphasizes that the ectomycorrhizal fungal communities show significant differences between roadside verges and forest communities, suggesting that planting modified trees in inappropriate areas may disrupt the existing native flora and fungal communities. For instance, the text mentions that roadside verges have a different complement of ectomycorrhizal fungi compared to forests, indicating potential ecological consequences of indiscriminate planting.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: We first considered symmetric traps as shown in Figure 7a and varied the strengths of the magnetic field and the trap depth d (by varying the overlap between two circular pillars with fixed radius r). In this case, p7. Escape from traps: Simulation(a) Trajectories of 100 bacteria escaping from the apex of a symmetric trap (B = 50 µT). The trap consists of two obstacles of radius 50 µm, in a distance of Δy = 100 µm. This results in a trap depth d = 50 µm. (b) Distribution of escape times of bacteria from the trap in (a). An exponential curve exp(−t/τ) (dashed line) is fitted to the data, with mean escape time τ = 22 s. (c) Dependence of the mean escape time τ on the field strength B for the trap in (a). An exponential curve is fitted to the data (dashed line). (d) Trajectories of 100 bacteria escaping from the apex of a generic trap (B = 50 µT). (e) Dependence of τ on d for B = 50 µT. Blue points show symmetric traps with both obstacles of radius 50 µm, similar to the trap in (a), gray points show a representative sample of all possible traps. An exponential curve is fitted to the blue data points (dashed line). Finally, we asked whether trapping of bacteria in corners is directly visible in our experiments. For that, we focused on one trap, indicated by the triangular area in Figure 8 (inset) and tracked the bacterial density in that area over time. Indeed, we could see bacteria trapped in this corner as indicated by a constant bacterial density in the trap. We then reduced the field strength in two steps, first from 500 µT to 50 µT and then to 0 µT. After both steps, we could observe a reduction of the bacterial density (Figure 8), indicating the expected release of bacteria from the trap upon reduction of the field and providing direct evidence for trapping in our experiments. Fig. 8. Escape from trap: ExperimentThe bacterial density in a trap (indicated by the yellow triangular area in the inset) is monitored over time. At the time points indicated by the arrows, the field strength is reduced (from 500 µT to 50 µT and from 50 µT to 0 µT, respectively), allowing the escape of trapped bacteria. [SEP] Justify:

Output: The text provided discusses bacterial trapping in magnetic fields, detailing experiments with traps and escape times. There is no mention of sea turtles or their navigation methods, making this text irrelevant to the claim about sea turtles navigating using the Earth's magnetic field.


Input: Task: corroborate the claim using the given text. Claim: Some wearable devices use heart rate and movement data to estimate sleep stages. [SEP] Text: Although PSG provides reliable data on sleep, other less laborious methods are needed as the increasing prevalence of sleep disorder diagnoses [15] has highlighted an urgent gap to be filled in the development of reliable, cost-efficient sleep study tools for both clinical and consumer use [16]. Some recent studies suggest that HRV might provide a noninvasive marker for detecting sleep behavior such as differentiating between sleep stages [10,11]. HRV has also been widely utilized for assessing phenomena such as stress and recovery [17], physical activity [18], and oxygen consumption [19]. Recently, a sleep analysis method was developed based on HRV and acceleration data (Oy, Jyväskylä, Finland) for providing personalized feedback and guidance regarding the quantity and quality of sleep. HRV as measured by a single-lead ECG device (2, Oy) can estimate atrial fibrillation accurately [20], making it a reliable measurement device regarding HRV-related phenomena. Based on the need to evaluate the validity of commonly available and easy-to-administer sleep measurement solutions, we investigated how PSG and the Firstbeat sleep analysis algorithm correlate in detecting sleep stages. From analog measurements, we estimated the sensitivity, specificity, and accuracy of the Firstbeat method in relation to PSG measurement over two nights. MethodsParticipantsThe study protocol has been described in detail in a previous publication [21]. We recruited 20 voluntary participants (mean age 24. 5 years, SD 3. 5, range 20-37 years; 10 [50%] women) by word of mouth in Helsinki, Finland. Participants were recruited from the research team’s circle of acquaintances: if the acquaintance showed initial interest in participating, they received a detailed description of the procedure via email. After reading the description, if the potential participant was still interested in taking part in the sleep study, they were screened for suitability. Their sleep was then measured for two consecutive nights using PSG, chest-worn 2, and a wrist-worn Geneactiv actigraph (Activinsights Ltd, Kimbolton, United Kingdom). The inclusion criteria were as follows: aged between 20 and 45 years, and a relatively stable sleep schedule (eg, no shift work or jet lag). [SEP] Justify:

Output: The text corroborates the claim by discussing the use of heart rate variability (HRV) and acceleration data in a sleep analysis method that provides personalized feedback regarding sleep quantity and quality. It explicitly mentions that HRV can be utilized for detecting sleep behavior, including differentiating between sleep stages, which supports the assertion that wearable devices utilize heart rate and movement to estimate these stages.


Input: Task: contrast the claim using the given text. Claim: Most modified trees can be planted anywhere without affecting native flora. [SEP] Text: The differences in soil and the management practices in roadside verges may explain this discrepancy. Eutrophication and litter accumulation cause a decrease in the species number, resulting in low species numbers in eutrophicated places and/or in thick litter layers, even under old trees. A new, preliminary classification of ectomycorrhizal fungi regarding their respective optima during the sucession of the site, based on the data from roadside verges is presented. In a homogeneous roadside verge with 100 year old along a canal the effects of various management treatments were studied. During the years 1987-'91, 5 different treatments were applied: a. mowing without removal of the hay, b. mowing with removal of the hay, c. nonrecurrent removal of sods in combination with mowing without removal of the hay, d. N-fertilization during the first 3 years in combination with mowing without removal of the hay, e. no treatment. Treatment "a" served as control. Each treatment was carried out sixfold in plots of 50x4. 5 m 2. The phanerogam vegetation, some soil properties and ectomycorrhizal samples from treatments b, c and d were analyzed as well. Removal of sods caused an immediate decrease of sporocarps of mycorrhizal fungi but after 3 years the species numbers had largely recovered. However, there was some shift in species composition. After fertilization a strong decrease of ectomycorrhizal fungi occurred. The saprotrophs were significantly afflicted by removal of sods. Fertilization resulted in a decline in saprotroph species numbers but the production did not change significantly. Treatments b and e showed no significant differences with the control. The soil chemistry was not influenced by the treatments except for higher concentrations of N in fertilized plots. In eight open beech plots, viz. four in plots rich in ectomycorrhizal species on nutrient-poor soil and four in species-poor and nutrient-rich soil, root samples were taken. In the field-experiment root samples were taken two years after the start of the treatments b, c and d. In the beech plots a positive correlation was found between the number of mycorrhizal root tips and the number of ectomycorrhizal species found during the fieldwork. [SEP] Justify:

Output: The text contrasts the claim by describing how management practices, such as removal of sods and fertilization, significantly affect the mycorrhizal fungi and saprotroph species in the soil. This indicates that planting modified trees, which may require specific soil conditions, could disrupt existing native flora and fauna, particularly regarding their associated fungal communities.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: 3 Concluding remarksIn this study, we have used experiments in microfluidic channels and p4. 1 Characterization of sediment4. 1. 1 Sample harvesting and preparationThe sediment sample was collected in the Großer Zernsee lake (Potsdam, Germany), from the first layer of sediment (5 cm) in the shallow water nearby the shore. Macroscopic organic matter was manually removed. The sand was stored in lake water, and shaken before sample preparation to avoid strong sedimentation effects. The sample of sand in water was placed in a plastic cuvette of 4 mm in diameter, with a layer of water covering the top of the sand. 4. 1. 2 Micro-computer tomographyThe micro-computer tomography scan was performed with the SkyScan 1172 scanner. For the scans, the following setting were used: X-ray source 89 kV, 112 µA, Size (µm)=1. 56, Exposure (ms)=1400, (°)=0. 150, with frame averaging. Raw data were reconstructed using NRcon software (Version 1. 6. 10. 4). For the reconstruction: (µm)=1. 56202, Range (deg)=360. 00, (deg)=0. 1500, Correction=10, Smoothing=0, Filter cutoff relative to Nyquist frequency=100, Filter type description=Hamming (Alpha=0. 54), Correction (%)=70. 4. 1. 3 3D statistical analysis with CTAnFor the statistics, the first 1316 bottom slices were used (2 mm of sample), to avoid slices not completely filled by sand and distortion effects that were seen in the top slices. The microCT images were processed by thresholding with Otsu method, despeckled (for white and black speckles, to reduce the noise) and a Median filter was applied with the the CTAn software (Version 1. 16, Brucker). The 3D statistics regarding the grain-size distribution (trabecular thickness) and the water-gap distribution (trabecular separation) was obtained by the CTAn software (Version 1. 16, Brucker). In this process, the grain sizes are quantified by the smallest dimension of the sediment grains. The distributions were fitted with a fitting tool, using a Gaussian fitting model a exp(−(x − µ)2/c2). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the methods and analysis related to sediment samples and does not provide any information regarding sea turtles or their navigation using the Earth's magnetic field.


Input: Task: corroborate the claim using the given text. Claim: Some wearable devices use heart rate and movement data to estimate sleep stages. [SEP] Text: Exclusion criteria were any diagnosed sleep disorder, the use of any medication that could affect sleep, acute sickness (eg, the flu), and gold allergy (as the electrodes used for the PSG recording were gold-plated). Each participant received a compensation of 100 euros (US $115) and structured feedback on their sleep stages. Written informed consent was obtained from all participants. The study was approved by the of the Central Hospital. All procedures followed were in accordance with the and its later amendments. In our previous study, we investigated two different intervention groups within this setting, and demonstrated that these groups did not differ significantly from each other [21]. Index (PSQI) scores of the participants, ranging from 2 to 12 points, indicate some variation in sleep quality. For the purpose of this study, all nights from all participants were pooled together for comparisons of PSG and Firstbeat sleep metrics. ProcedureA research assistant visited participants at their homes on two consecutive nights. Participants had been asked not to consume alcohol or caffeine after 4 PM on the measurement nights. The evening visit started between 6 and 10 PM, depending on the participant’s current sleeping schedule. The research assistant attached the measurement electrodes to the participant during the house call and began the recording. Before the research assistant left, participants were given instructions for the night; the participants were instructed to spend the evening as usual but to refrain from vigorous activities. They were also asked to keep their phones and any other electrical devices with transmitters at least 2 meters away from the bed so they would not interfere with the PSG recording. Participants were instructed to sleep normally, and the visit for the following morning was scheduled according to the participant’s expected wake-up time. The research assistant arrived the following morning approximately 0 to 30 minutes after the wake-up time. Physical MeasurementsWe measured HRV with 2, including two chest electrodes and 3-axis acceleration data obtained from the wrist with a Geneactiv actigraph. sleep analysis method evaluates the physiological state of the person as being awake or asleep based on HRV and acceleration data, and scores sleep as N1+N2 (light), N3, or REM. The method uses a neural network–based algorithm with -derived respiration rate, movement, and time of day data for sleep and wake detection and for sleep classification. [SEP] Justify:

Output: The text corroborates the claim by stating that a sleep analysis method evaluates the physiological state of a person as awake or asleep based on heart rate variability (HRV) and acceleration data. Additionally, it mentions that sleep is classified into stages of light, deep, and REM sleep using a neural network-based algorithm that incorporates movement data, which aligns with the claim regarding the usage of heart rate and movement data in wearable devices for estimating sleep stages.


Input: Task: contrast the claim using the given text. Claim: Most modified trees can be planted anywhere without affecting native flora. [SEP] Text: A comparison of the fungal communities of roadside verges with forest communities of the same tree species was made. Comparison with Dutch oak forest communities revealed that 42 ectomycorrhizal species were found to be differential for roadside verges and 39 for forests. 16 species were indifferent. Among the saprophytes, 24 species were differential for roadside verges, at least 62 for forests and 24 were indifferent. Only the Dicrano-Quercetum showed resemblance with some types of oak plots. The main difference with the other types of oak forest is the larger number of ectomycorrhizal fungi in roadside verges. Regarding the saprotrophic fungi, roadside verges differ profoundly from forests. Terrestrial raw humus inhabiting and lignicolous fungi are mostly restricted to forests, whereas typical grassland fungi were mostly found in roadside plots. Dutch oak forest communities showed more similarity with the roadside verge communities than those from other parts of Europe. Generally, communities with little or no organic layers on nutrient-poor soils have a large proportion of ectomycorrhizal species in common with the analogue roadside communities. A classification is presented for ectomycorrhizal roadside fungi based on their assumed restricted occurrence in the roadside habitat. In roadside verges a number of threatened fungi occur that presumably have disappeared from forests with the same tree species. In 25 plots of the Hypochaeris radicata-Quercus type with trees of three different ages successional series were studied. The number of ectomycorrhizal species increases with the tree age. This is in contrast to data from forests in the literature, where the species number decreases after an initial increase. The differences in soil and the management practices in roadside verges may explain this discrepancy. Eutrophication and litter accumulation cause a decrease in the species number, resulting in low species numbers in eutrophicated places and/or in thick litter layers, even under old trees. A new, preliminary classification of ectomycorrhizal fungi regarding their respective optima during the sucession of the site, based on the data from roadside verges is presented. In a homogeneous roadside verge with 100 year old along a canal the effects of various management treatments were studied. During the years 1987-'91, 5 different treatments were applied: a. mowing without removal of the hay, b. mowing with removal of the hay, c. [SEP] Justify:

Output: The text contrasts the claim by highlighting significant differences in fungal communities between roadside verges and forest environments, suggesting that planting modified trees in these different habitats could disrupt native fungal species. It states that roadside verges and forest communities harbor different ectomycorrhizal and saprotrophic fungi, which implies that the introduction of modified trees could affect the composition and health of native flora. The discrepancies in species distribution and management practices discussed further support the idea that planting these trees indiscriminately could lead to ecological consequences.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: For the grain-size, the fitting was performed on the distribution of the logarithm of the size, while for the water-gaps the fitting was performed on the gap size. The 3D visualization was done with the Amira software. 4. 1. 4 2D statistical analysis2D image preparationThe 2D slices obtained from the microCT reconstruction at different regular depths (0, 624, 1248 and 1872 µm) from the bottom of the cuvette, Fig. 1a) were first processed with ImageJ: the images were cropped in the center to avoid the border effects, they were binarized to get black and white masks and a Median filtering was applied for smoothing Fig. 1b. Circle fittingSubsequently, the images were analyzed by a custom-made program (Fig. 1c): erosion was applied to separate the grains from each other (settings: diamond shape and radius 9 pixels). Subsequently, the centroid function provided the center of each grain and the radius of a circle with equivalent area. Grain size statisticsThe radii of the circular fit were used for calculating the statistics of the slice (Fig. 1d). The percentage of sand was calculated as black pixels number over total number of pixel times 100. While the 3D analysis method estimates the smallest dimension of the grain and thus underestimated grain size, the 2D method slightly overestimates the biggest dimension of the grain (see Fig. 2g). 4. 2 Microfluidic channel4. 2. 1 Mask template preparationThe 2D microCT binarized and processed images (see section 4. 1. 4, Fig. 1b), and the binarized images (Fig. 1e) obtained from the circle fitting (see section 4. 1. 4, Fig. 1c) were adapted to avoid air bubble formation and to improve water perfusion, with water-gaps manually increased in critical points. These binary figures were used as a template to design the photomask in AutoCAD 2015 obtaining 1330 µm-wide microchannels Fig. 1f. 4. 2. 2 Master mould productionA high resolution chrome photomask was obtained from GmbH and was used to produce a master mould with 10 µm deep features. The master mould was fabricated by baking a silicon wafer at 200 °C for 20 min and allowing it to cool down to room temperature. [SEP] Justify:

Output: The text passes in relevance to the claim because it discusses technical aspects of data analysis methods and image processing, which do not relate to how sea turtles navigate or the Earth's magnetic field. Therefore, it neither supports nor contradicts the claim.


Input: Task: corroborate the claim using the given text. Claim: Some wearable devices use heart rate and movement data to estimate sleep stages. [SEP] Text: All comparisons of sleep staging between PSG and Firstbeat were performed from the PSG-measured actual sleep onset onward. First, we used paired-sample t tests to compare sleep metrics for evaluating differences between PSG and Firstbeat in sleep onset, and minutes spent in wake, light sleep, SWS, and REM sleep. Second, we conducted epoch-by-epoch comparisons between Firstbeat and PSG to calculate the sensitivity (ability of Firstbeat to detect true sleep), specificity (ability of Firstbeat to detect true wake), and accuracy (ability of Firstbeat to detect both sleep and wake). This comparison was performed across all sleep stages, as well as for overall sleep-wake comparisons between PSG and Firstbeat. Third, we used a confusion matrix to compare epoch-by-epoch measures of true positives, true negatives, false positives, and false negatives between Firstbeat and PSG across all measured nights for sleep versus wake as well as for light sleep, SWS, and REM sleep stages. True positives arise when both the PSG and Firstbeat score the 30-second epoch as sleep. True negatives arise when both the PSG and Firstbeat score the epoch as awake. False positives arise when the PSG scores the epoch as sleep but Firstbeat scores it as wake. False negatives arise when the PSG scores the epoch as wake but Firstbeat scores it as sleep. Fourth, we evaluated the differences between the amount of sleep scored as wake, light sleep, SWS, or REM sleep when comparing Firstbeat and PSG using minute-based Bland-Altman plots, and visually demonstrate how many observations remained within a 30-minute window of the PSG measure. Finally, we used t tests to compare whether specificity, sensitivity, and accuracy differed based on sex, measurement night, or the intervention we reported previously [21]. ResultsThe 40 nights from 20 participants measured with both PSG and Firstbeat were included in all analyses with no exclusions. Table 1 shows the participants’ characteristics as well as their mean sleep measures. Table 1. Characteristics of the sample (N=20). CharacteristicValueAge (years), mean (SD)24. 50 (3. 50)Sex (female), n (%)10 (50)BMI (kg/m2), mean (SD)23. 64 (3. 10)PSQIa score, mean (SD)5. 40 (2. [SEP] Justify:

Output: The text corroborates the claim by outlining a study that compares sleep metrics derived from PSG and Firstbeat. The analysis includes the ability of Firstbeat to detect true sleep, wake, and different sleep stages (light sleep, SWS, and REM sleep), indicating that wearable devices are indeed using data to estimate sleep stages, which aligns with the claim about wearable devices.


Input: Task: pass the claim using the given text. Claim: These trees capture ten times the carbon of regular trees in any environment. [SEP] Text: arrays Since vectors are automatically perceived as an attempt to use R vectorization, any method or constructor involving an array will be misinterpreted. Arrays class provides many static methods to manipulate arrays. The argument of these static method is obviously an array. For example, the sort method makes it possible to sort the values within an array of doubles. Because vectors are not automatically converted into arrays in J4R, the following line of code will inevitably throw an exception: > callJavaMethod(“java. util. Arrays”, “sort”, c(22,10,14)) Error in. checkForExceptionInCallback(callback) : java. lang. NoSuchMethodException: Method sort cannot be found in the class Arrays > The array must be instantiated first and then passed to this method. This can be done through the as. JavaArray function of the J4R package. The function returns a pointer to the array instance which can be passed to a method or a constructor as follows: > myArray <- as. JavaArray(c(22,10,14)) > myArray [1] “One-dimension array of D@1799180931” > callJavaMethod(“java. util. Arrays”, “sort”, myArray) > getAllValuesFromArray(myArray) [1] 10 14 22 > The myArray variable is a pointer to an array of doubles. Calling the sort method in Java sorts the values of the array in ascending order. The reconversion to R is obtained through the getAllValuesFromArray function. Note that the as. JavaArray function accepts vectors and matrices of any primitive types listed in Table 2. Interrupting native code R is meant to handle the interruption of native code in C and C++ (Wickham, 2015, Ch. 10). To the best of my knowledge, there is no guideline for other languages. J4R implements a workaround that allows for interruption of native Java code. In this example, the thread that handles to code on the Java end is asked to “sleep” for a 100 s: > callJavaMethod(“java. lang. Thread”, “sleep”, as. long(100000)) Note that the sleep method takes a long as argument. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses programming concepts related to arrays in the context of Java and R, with no mention of trees, carbon capture, or environmental topics. Thus, it does not support, contrast, or relate to the claim in any way.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: An SU-8 3010 (MicroChem Inc. ) thin film was then spin-coated onto the wafer to a height of 10 µm (spincoating parameters: 15 s at 500 rpm, followed by 30 s at 3,000 rpm), followed by a soft-bake (1 min at 65 °C, followed by 3 min at 95 °C, and 1 min at 65 °C), exposed for 5 s to UV light through the chrome mask with a mask aligner (Kloé UV-KUB 3) according to the manufacturer recommendations, and post-exposure bake (1 min at 65 °C, followed by 3 min at 95 °C, and 1 min at 65 °C). The wafers were then developed with mr-Dev 600 (microresist technologies). Prior to their use, the master molds were treated with 1H,1H,2H,2H-perfluorodecyltriethoxysilane 97% (abcr) to reduce PDMS adhesion upon usage. 4. 2. 3 Microsystem fabricationThe microfluidic device was produced with polydimethylsiloxane (PDMS) via soft lithography. Briefly, PDMS elastomer monomer and curing agent (Sylgard 184, Dow Corning) were mixed in a ratio 10:1 and then degassed. The PDMS was casted onto the master mold to a height of 5 mm and was cured at 80 °C for 2 h. The inlets for the fluidic channel were then punched with a 1. 5 mm diameter biopsy punch (pmfmedical). The PDMS was bonded to a clean glass slide by plasma activation to finish the microfluidic system. 4. 3 Swimming in sediment-like obstacle channels4. 3. 1 Bacterial cultureMagnetospirillum gryphiswaldense MSR-1 was cultured in MSR-1 growth medium with the composition indicated by Heyen and Schüler [42], with the addition of pyruvate (27 mM) as carbon source instead of lactate. For the creation of an aerotactic band and the subsequent selection of swimming bacteria, the growth medium was supplemented with 0. 1% agar. [SEP] Justify:

Output: The text is irrelevant to the claim, as it discusses the fabrication of a microfluidic device and the culture of bacteria rather than addressing any aspects of sea turtles or their navigational methods. The content does not mention sea turtles or their relationship with the Earth's magnetic field.


Input: Task: corroborate the claim using the given text. Claim: Some wearable devices use heart rate and movement data to estimate sleep stages. [SEP] Text: 35)Poor sleep quality (PSQI score>5), n (%)5 (25)Polysomnography-measured sleep, mean (SD)Sleep onset (hour:minute)23:44 (1:05)TSTb (hour:minute)7:28 (0:49)Sleep efficiency (%)91. 55 (5. 82)N1c of TST (%)4. 68 (2. 71)N2d of TST (%)47. 46 (6. 14)N3e of TST (%)22. 18 (7. 11)REMf of TST (%)25. 68 (5. 48)aPSQI: Quality Index. bTST: total sleep time. cN1: sleep stage 1 (light sleep). dN2: sleep stage 2 (light sleep). eN3: sleep stage 3 (slow wave sleep). fREM: rapid eye movement. Table 2 shows paired t test comparisons and the mean differences between Firstbeat and PSG sleep stage scores. Sleep onset did not differ significantly between Firstbeat and PSG (mean difference 0, SD 9 minutes, SE 1 minute; t39=0. 578, P=. 57). Three nights’ sleep onset was detected accurately, whereas for 12 nights, the Firstbeat method assumed earlier sleep onset than PSG. To detect the true difference in detecting sleep onset, we calculated the absolute difference between Firstbeat onset to PSG onset, and found a mean difference of 7. 06 minutes (SD 6. 64 minutes). Table 2. Paired comparisons and mean differences of sleep parameters recorded by Firstbeat and polysomnography. Parameter (minutes)Mean differencea (SD)SE95% CIt (df=39)P valueWake14. 03 (16. 65)2. 638. 70 to 19. 355. 327<. 001Light sleep–0. 80 (42. 25)6. 68–14. 31 to 12. 72–0. 120. 91Slow wave sleep4. 68 (46. 79)7. 40–10. 29 to 19. 640. 632. 53REMb sleep–17. 90 (50. 44)7. 98–34. 03 to –1. 77–2. 244. 03aMean differences calculated as Firstbeat – polysomnography. bREM: rapid eye movement. [SEP] Justify:

Output: The text corroborates the claim by discussing the Firstbeat method, which is a wearable device that measures sleep stages through accuracy comparisons with polysomnography. It also mentions specific data related to sleep stages (N1, N2, N3, REM), which indicates the device's capability to assess sleep quality and stages utilizing heart rate and movement data.


Input: Task: contrast the claim using the given text. Claim: These trees capture ten times the carbon of regular trees in any environment. [SEP] Text: In contrast, the TRW mean shows a better relation with summer temperatures which means that the air temperature has a dominant influence on tree ring growth but not on the isotope composition of LW cellulose. The offset defined as difference of δ13CLW and δ13CEW (Figs. 2, 3) is expected to be negative since the δ13CEW signal is believed to be more positive than the δ13CLW signal due to a contribution of the enriched reserves (Rossmann et al. 1991; Tcherkez et al. 2004) used for the leaf and tree ring formation at the start of the vegetation period. Theoretically, under constant climate conditions the mean offset should remain the same but with moderate inter-annual variability. Indeed we observed a constant offset with higher fluctuations induced by sudden decreases and increases of the δ13CLW isotopic signal compared to the δ13CEW signal for the period ad 1780–1825. These fluctuations may be driven by inter-annual changes of site meteorological conditions or more plausible by weather extremes, e. g. volcano eruptions (Laki ad 1783, unidentified eruption ad 1809 and Tambora ad 1815) for the case of negative deviations. In agreement to the temperature decreases observed after eruptions, δ13CLW shows more negative values. Moreover, in contrast to δ13CLW, the TRW Sa1 time series documents positive correlations with summer temperature, indicating that temperature influences the tree ring growth but not the carbon isotopic composition of cellulose (Fig. 4). No correlations were found for the offset with temperature (highly variable) but a negative correlation with summer months’ precipitation. In contrast, the δ13CLW–δ13CEW offset is different during ad 1960–1994 by exhibiting a positive trend with similar short-term fluctuations as for the cold period. The trend is, however, mainly determined by some values in the 1980’s. Nevertheless, it could indicate an influence of changed conditions (increase in temperature and CO2 concentrations) that might lead to a lower dependence of EW on reserves of previous years documented by the lack of dependence shown in Fig. 7. Correlations with summer temperatures improved compared to the cold period but are only partly significant whereas correlations with summer precipitation remained significant, which supports our expectation that the hydrological conditions have not changed over time at the site. [SEP] Justify:

Output: The text contrasts the claim by discussing the relationship between temperature and tree ring growth, as well as the carbon isotopic composition of cellulose. It emphasizes that although temperature influences tree ring growth, it does not affect the isotopic composition in the expected manner, thus challenging the notion that these trees are inherently superior in carbon capture capabilities compared to regular trees.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: Briefly, 1 mL of bacteria was inoculated into the bottom of a 15 mL Hungate tube filled with 10 mL of MSR-1 growth medium with 0. 1% agar. The tube was sealed with a rubber cap pierced with a needle capped with a 0. 2 µm filter (Whatman) to allow the formation of an oxygen gradient. To select the North-seeking bacteria (i. e. swimming with the North of their magnet at their front in oxic conditions) in the formed band, the tube was put inside a pair of coils to apply a magnetic field parallel to the oxygen gradient, but pointing downwards to the anoxic region (equivalent to the situation that the bacteria experience in their natural habitat in the Northern Hemisphere). The bacteria were grown at 28 °C and allowed to form an aerotactic band. Once the band had formed, the motile bacteria were selected by aspirating the band with a needle and culturing them for two passes in standard MSR-1 growth medium and microaerobic conditions. Subsequently, the magnetic bacteria were collected with a needle by placing a magnet next to the tube to attract them. The final population of North-seeking bacteria was estimated to be about 80% of the population. In oxic conditions as the ones in our experiment (where oxygen can freely diffuse through the PDMS into the microchannel), these bacteria swim with the North of their magnet at their front. The optical density was measured at 565 nm (OD565) and was adjusted as needed with fresh growth medium for subsequent measurements. 4. 3. 2 Operation of the chipThe microfluidic system was filled with a syringe pump with MSR-1 standard growth medium at a flow rate of 15µL·min-1 and was incubated at room temperature for 30 min prior to its use. Subsequently, 25 µmL of MSR-1 standard growth medium containing bacteria at an OD565 of 0. 1 were placed in the inlet and the bacteria were allowed to swim into the microchannel, placed under a custom-designed magnetic microscope equipped with three pairs of Helmholtz coils [27]. Different magnetic fields intensities (B = 0, 50, 500 µT) were applied parallel to the microchannel length. Bacteria were imaged with a 10x objective using phase contrast microscopy. 4. 4 Model and simulations4. 4. [SEP] Justify:

Output: The text is irrelevant to the claim about sea turtles navigating using the Earth’s magnetic field. It discusses the growth conditions of certain bacteria in a microfluidic system and the method used to select North-seeking bacteria based on a magnetic field. There is no mention of sea turtles or their navigation methods.


Input: Task: corroborate the claim using the given text. Claim: Some wearable devices use heart rate and movement data to estimate sleep stages. [SEP] Text: When comparing Firstbeat and PSG, there were some differences in how well the Firstbeat method was able to detect different sleep stages. The mean specificity, sensitivity, and accuracy in detecting wake was 0. 77 (SD 0. 16), 0. 95 (SD 0. 03), and 0. 93 (SD 0. 03), respectively. The specificity, sensitivity, and accuracy in detecting light sleep was 0. 69 (SD 0. 15), 0. 66 (SD 0. 10), and 0. 69 (SD 0. 06), respectively. The specificity, sensitivity, and accuracy in detecting SWS was 0. 91 (SD 0. 06), 0. 72 (SD 0. 17), and 0. 87 (SD 0. 04), respectively. REM sleep was detected with 0. 92 (SD 0. 7) specificity, 0. 60 (SD 0. 24) sensitivity, and 0. 84 (SD 0. 06) accuracy. Table 3 shows the confusion matrix [22] regarding the two measurement devices and their differences. Table 3. Confusion matrix of the Firstbeat method and polysomnography sleep stage epoch comparisons. Firstbeat (N)Polysomnography (N)12,737c2189344549218,863SWS26835867c40968686REM20721315576c2057984Wake14351253552598c4513Total18,92783129416339140,046cCorrect stage classification (%)67. 370. 659. 276. 6N/AdaSWS: slow wave sleep. bREM: rapid eye movement. cDiagonals indicate the number of correctly categorized epochs in the respective sleep stage. dN/A: not applicable. Figure 1 shows the Bland-Altman mean difference plots, which illustrate the share of observations that were within 30 minutes from each other in wake state, or in different sleep stages as measured with different devices. Figure 1. Bland-Altman plots comparing differences in Firstbeat (FB) and polysomnography (PSG) in wake state (a), light sleep stages N1+N2 (b), slow wave sleep (SWS) (c), and rapid eye movement (REM) sleep (d). [SEP] Justify:

Output: The text corroborates the claim by providing specific details on how the Firstbeat method compares to polysomnography in detecting various sleep stages, suggesting that the Firstbeat device can effectively estimate sleep stages based on parameters such as specificity, sensitivity, and accuracy. This supports the idea that wearable devices utilize data to assess different sleep stages.


Input: Task: contrast the claim using the given text. Claim: Air purifiers with HEPA filters can remove airborne pollutants indoors. [SEP] Text: 5 µm p0. 75 for all p18. However, in our study different sizes of airborne p0. 015; for >10 CFU, 0. 037; and for >100 CFU, 0. 053. The sensitivity for these points were 95. 83%, 100%, and 100%; and the were 0. 51, 0. 88, and 0. 75, respectively16. These results were in line with our study results. Nevertheless, neither the different sizes of p18. Specifically, the first of these two studies was carried out by Mirhoseini et al. in the ICUs, operating rooms, and internal medicine areas in four hospitals in Iran. A total of 80 samples were collected and the average level of bacteria was between 75–1,194 CFU/m3 17. Although they did not include the presence of opportunistic filamentous fungi, they took into account the different airborne p18. In the same way, Armadans-Gil et al. founda strong correlation between the number of fungi and the airborne p0. 5 µm and 1 µm inoperating rooms, burn rooms or hematology rooms, as well as harmacy clean rooms19. They took 42 simultaneous samples: 24 in operating rooms, 13 in hematology wards, 3 in rooms, and 2 in other procedure rooms. The association between the airborne p0. 5 µm p0. 004) and 1 µm p0. 003). Likewise, Birgand et al. performed a large multicenter study with different surgical procedures. In this case, 3 microbiological air counts and 3 airborne p0. 3 μm, 0. 5 μm, and 5 μm p20. These results concur with our findings. Finally, Dai et al. also found that the number of biologic p0. 76). However, they did not take into account the p21. On the other hand, Landrin et al. , found no correlation in a study performed in 2005, but this study was only carried out during a period of 3 months in 4 operating rooms with HEPA filters in which only the concentration of opportunist fungi and airborne p0. 5 µm were studied22. Cristina et al. did not find a significant correlation between airborne p0. 5 µm and ≥5 µm were considered in a three-month study in an operating theatre for arthroplasty23. [SEP] Justify:

Output: The text contrasts the claim by citing a study that found no correlation in a situation where HEPA filters were used in operating rooms, indicating that despite the presence of HEPA filters, specific airborne pollutants (opportunistic fungi and particles of size 0.5 μm) were not significantly reduced. It specifically notes that 'Landrin et al. found no correlation in a study performed in 2005', which suggests that HEPA filters may not be effective in all indoor environments for removing certain airborne pollutants.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: 1 Equations of MotionWe model a magnetotactic bacterium as an active Brownian sphere in two dimensions that has a permanent dipole moment µ = µ ê with the magnetic strength µ at the p0. The orientation of the p1. Table 1Model parameters and their values. 4. 4. 2 SimulationsWe solve the equations of motion in two dimensions by using overdamped Brownian dynamics (BD) simulations. The following reduced units were used: time where is the translational diffusion constant; position r* = r/σ; ε* = ε/kBT; field strength B* = B/B0, where B0 = 1 × 10−5 T is the order of magnitude of the magnetic field strength of the earth; dipole strength µ* = µB0/kBT. The dimensionless equations of motion were integrated for Nsteps = 5 × 106 time steps using the Euler-Maruyama method [46]with a time step Δt* = 2 × 10−5. We note that, with this choice of dimensionless units, the self-propulsion velocity is identical to the Péclet number. For simulations of swimming through obstacle channels, we use spherical obstacles with the same positions and diameters as the cylindrical pillars in our microfluidic channels. We simulated all 10 different obstacle arrays. In each case, we initiated 2000 bacteria at position x = 0 corresponding to the IN region and tracked their motion until they arrived at the end of the channel (with x ≥ 3495 µm) or until the maximal simulated time (of 30 min). For simulations of the escape from traps, traps with random parameters were constructed from two spherical obstacles as shown in Figure 7 (a) and (d). We simulated the escape of 100 bacteria from each trap to determine the escape time. 4. 4. 3 Sliding measurements and parameterization of obstacle interactions in the simulationsFigure 4 b) illustrates how we determined the sliding distance. The sliding distance is defined as the arc length (solid green arrow) that is covered by the center of mass trajectory (solid blue line) inside the sliding region (dotted green line). The sliding region was chosen to be slightly larger than the interaction region (orange dotted line), where FS > 0 and thus τS > 0. The sliding region was set to 2 µm, when measured from the obstacles’ surface. [SEP] Justify:

Output: The text provided discusses a model of magnetotactic bacteria and their simulations in relation to magnetic fields but does not mention sea turtles or their navigation methods. Therefore, it is irrelevant to the claim about sea turtles using the Earth's magnetic field for navigation.


Input: Task: corroborate the claim using the given text. Claim: Some wearable devices use heart rate and movement data to estimate sleep stages. [SEP] Text: View this figureAs a further sensitivity check, we compared the means of specificity, sensitivity, and accuracy to detect possible differences based on sex, measurement night, or the intervention reported previously [21]. Compared to females, there was a better specificity in REM sleep in male participants (0. 95 vs 0. 89, P=. 004), but there were no other differences between sexes (P>. 06). There was no first- or second-night effect in the specificity, sensitivity, and accuracy (all P>. 38), nor regarding the presence of the previously reported music or slow-breathing intervention (all P>. 13). DiscussionPrincipal FindingsWearable devices have gained a significant share of the health and well-being consumer market, and new wearable devices and algorithms emerge frequently. Although a great majority of this research aims to detect sleep quality and duration based on data derived from accelerometer sensors [23], other measures such as respiratory signals have also been utilized [24]. Several reviews have evaluated the accuracy of accelerometer-based sleep wearables [23,25,26], and a recent review summarized an overall evaluation of wearables utilizing other sensors [27]. They concluded that detecting sleep from wake is relatively successful in many devices, but when wearables aim to classify sleep stages as opposed to simply distinguish between sleep and wake, there is a challenge in distinguishing four choices (wake, light, deep, and REM sleep) [27], which makes the result more inaccurate. Commercial accelerometers typically yield accuracy between 0. 81 and 0. 91, sensitivity values between 0. 87 and 0. 99, and specificity values between 0. 10 and 0. 52 in distinguishing sleep from wake [26]. However, when attempting to detect sleep stages, the results are less consistent. A recent review focusing on commercial accelerometers identifying sleep stages found great variation in accuracy depending on the study [26]. For instance, accuracy in detecting light sleep varied between 69% and 81%, accuracy in detecting SWS was between 36% and 89%, and that for REM sleep ranged between 62% and 89%. Such variation suggests that acceleration itself may not be sufficient in reliably identifying sleep stages. Previous studies have implied that HRV may be a useful marker for detecting sleep stages [10,11]. One study reported an accuracy of up to 89% in detecting SWS, but their method included respiratory signals alongside HRV [28]. [SEP] Justify:

Output: The text corroborates the claim by discussing how wearable devices, specifically those using accelerometer sensors and heart rate variability (HRV), aim to detect sleep quality and classify sleep stages. It mentions that while accelerometers can distinguish between sleep and wake, there are challenges in accurately classifying different sleep stages (wake, light, deep, and REM sleep). The text also implies that HRV may be useful in this classification, supporting the notion that heart rate data can contribute to estimating sleep stages.


Input: Task: pass the claim using the given text. Claim: Air purifiers with HEPA filters can remove airborne pollutants indoors. [SEP] Text: For natural ventilation, air change rates are incredibly difficult to predict due to uncertainties around wind speed, pressure coefficient, air temperature and ventilation area (Clements-Croome 2005; HM Government 2015). With mechanical ventilation, various guidance bodies specify a minimum air supply criteria in litres per second per person with UK building regulations (Part F) suggesting a minimum of 10/L/s/per person (HM Government 2015). Taking into account volumetric loading differences (Girman 1992) between the test chamber (0. 15 m3) and the small office (15 m3), the rate of NO2 removal is reduced by a factor of 100. Therefore, using measured removal rates (Fig. 2) and reducing by a factor of 100 allows us to derive the removal rate in a small office (Table 4). We therefore estimate that 1 m2 of the highest NO2 removing taxa per m2—Zamioculcas zamiifolia—in optimal environmental conditions, namely ‘typical’ light and ‘wet’ growing medium, would reduce a concentration of 100 ppb by 3 ppb after 1 h (data not shown). Furthermore, the highest removing potted plant (not considering leaf area), namely Dracaena fragrans ‘Golden Coast’ potted in wet growing media under ‘typical’ light conditions, was able to reduce a concentration of 100 ppb by 0. 62 ppb after 1 h. Results from all plant types under these presumed conditions are presented below, in Table 4. Table 4 The derived ability of each studied potted plant to reduce a concentration of 100 ppb inside a 15 m3 room in ‘wet’ (SMC > 30%, 0. 3 m3 m−3) substrate moisture conditions at ‘typical’ (~ 500 lx) light levelsFull size tableDiscussionInterpretation of chamber-based experimental resultsThis work investigates the ability of three common indoor potted plants to remove—in real-time, over a period of 1 h—an in situ concentration of 100 ppb of NO2 from a 150-L chamber. We demonstrate that the studied potted plants are able to remove significant amounts of NO2 under common indoor conditions, i. e. 0 and 500 lx. As per the hypothesis, different taxa were able to remove NO2 at differing rates—per m2 of leaf area (Fig. [SEP] Justify:

Output: The text does not address air purifiers or HEPA filters specifically. Instead, it discusses the ability of indoor potted plants to remove NO2 from a chamber. While it does relate to indoor air quality, it does not corroborate or contrast the claim about HEPA filters or air purifiers.


Input: Task: pass the claim using the given text. Claim: Many traditional medicines are derived from plants used by indigenous cultures. [SEP] Text: Prior to administration to test animals, the extract was prepared by dissolving the powder in normal saline. H. polyrhizus fruits were peeled and the flesh was blended for 10mins with chilled acetone: water (80:20) in a ratio of 1 part flesh to 4 parts 80% acetone. The homogenate was centrifuged at 3000g for 10min at 4°C. The supernatant was filtered in vacuo and the acetone evaporated under reduced pressure at 40°C followed by lyophilization. The powder was dissolved in normal saline prior to administration to test animals. The extract doses were as follows: O. sumatrana : 1g/kg body weight. S. polyanthum: 1g/kg body weight. H. polyrhizus: 3g/kg body weight. (α-tocopherol) was prepared in extra virgin olive oil in a dose of 250mg/kg body weight17 and was given via oral feeding needle, once a day continuously for 2weeks. In all cases, animals were killed at 24h following the last dose. Animals were anaesthetized, the abdomen was opened and the liver and kidney were taken, cut into small pieces and fixed in Orth's fixative. They were then processed and embedded in paraffin. Tissues were cut into 2-3µ thick sections. Five sections were randomly chosen from each animal of each group (30 slides per group), sections were mounted on slides and stained with Haematoxylin and Eosin (H&E). They were studied by light microscopy. Histological changes found in at least 70% of the slides (21 of 30 slides) in each group were taken as significantly positive results (Chi-square test). Grading of liver histological alterations was done as previously described18 as follows: Mild damage: low to moderate grade of necrosis. Moderate damage: centrolobular lesion. Severe damage: extensive to very extensive damage, with destruction of hepatocytes in extensive areas or throughout the liver. Kidney alterations were described accordingly to mild, moderate and severe damage. assessments Normal control rats, control diabetic rats and non diabetic rats treated with vitamin E: These animals showed normal liver. Histological examination revealed that the liver was formed of lobules; in the centre of each was a central vein. [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily discusses an experimental procedure involving the preparation and testing of plant extracts on animals. No information is provided regarding the derivation of traditional medicines from plants used by indigenous cultures.


Input: Task: corroborate the claim using the given text. Claim: Some wearable devices use heart rate and movement data to estimate sleep stages. [SEP] Text: When detecting sleep stages by utilizing both HRV and accelerometer data, one study managed to identify 75% of SWS correctly [29]. In that study, REM sleep was identified correctly in over 70% of epochs, whereas light sleep detection was the weakest with correct identification varying between 42% and 52%. Our findings are of similar accuracy, which further supports the notion of combining accelerometer and HRV-based measures for reproducible sleep staging. This study was performed to evaluate the ability of HRV- and acceleration-based Firstbeat sleep analysis methods to detect sleep and different sleep stages. In pairwise comparisons, the Firstbeat method detected light sleep and SWS with no statistically significant difference to the gold-standard PSG method. There were two measures that differed significantly between the Firstbeat method and PSG: Firstbeat underestimated REM sleep (mean 18 minutes) and overestimated wake (mean 14 minutes). Considering the number of minutes in the context of a typical night’s sleep, the differences are not alarmingly high in practice, especially when measuring sleep over repeated nights. Sleep onset detection was very accurate, which is in accordance with a review published earlier this year [30]. Sleep stages can only be detected using PSG, as the stages are, by definition, separated by different patterns in and ECM. REM sleep is particularly difficult to detect without measuring activity from EOG and EMG channels. Thus, relying on other physiological measures as a means for separating sleep stages is always based on secondhand information. Although HRV has both previously [10,11] and in this study reflected sleep stages relatively well, it cannot detect the immediate changes in and EMG. However, this study suggests that HRV-assisted sleep stage detection can serve as a good estimate of sleep architecture despite being less accurate in detecting specific sleep stages. When observing the comparisons in more analytical detail, we found that comparing the Firstbeat method against PSG yielded good specificity, and excellent sensitivity and accuracy in detecting wake. Regarding light sleep, the measures of specificity, sensitivity, and accuracy were less convincing. SWS detection had excellent specificity, adequate sensitivity, and good accuracy, while REM sleep was detected with similarly excellent specificity, adequate sensitivity, and good accuracy. These results suggest that the Firstbeat method is best at detecting sleep stages that have strong parasympathetic cardiac markers; however, light sleep is typically not significantly differentiated based its physiological fingerprint [12,14]. [SEP] Justify:

Output: The text corroborates the claim by describing how HRV (Heart Rate Variability) and accelerometer data are utilized for detecting sleep stages. It mentions that studies have successfully combined these physiological measures to estimate sleep architecture and indicated findings that support this method of sleep staging. Specifically, it discusses the accuracy of the Firstbeat method in detecting sleep stages using HRV and movement data, aligning with the claim about wearable devices using such data.


Input: Task: pass the claim using the given text. Claim: Air purifiers with HEPA filters can remove airborne pollutants indoors. [SEP] Text: At first sight, such a removal rate may look relatively low, but it should be noted that the measured NO2 removal occurred in typical light and even dark conditions whilst NO2 exposure peaks tend to appear over short, often rush-hour-related periods (Malley et al. 2018; Engström and Forsberg 2019). It can therefore be expected for any removal to be constant throughout the day or night, even when plants are under mild water deficit—unlike with CO2 where supplementary lighting was required for CO2 uptake to occur (Gubb et al. 2018, 2019). Therefore, plants are able to remove NO2, without additional energy requirements. Furthermore, as plant type seems to have little effect (Table 4) on NO2 removal at the office scale (considering the plant types in this study only), easier-to-maintain plants like Zamioculcas zamiifolia and other succulents would likely be just as effective. Comparing to our earlier work on the potted plants’ ability to remove carbon dioxide (CO2) indoors (Gubb et al. 2018, 2019), the fact that removal of NO2 occurs at typical indoor light levels is a significant advantage. Without the need for supplementary lighting, increasing both energy costs and integration difficulties for designers, passive NO2 mitigation via potted plants is a much more viable technique—especially with the built environments push towards net-zero carbon buildings. Our work is consistent with recent experiments that have suggested that the growing media and the microorganisms within are predominantly involved in the removal of pollutants and plants themselves are only utilised in-directly to maintain and support growing media microorganisms (Irga et al. 2018; Kim et al. 2018). As both the concentration of NO2 and light level—albeit, > 500 lx—affect the removal ability of plants (Teklemariam and Sparks 2006; Gourdji 2018; Hu and Sun 2010), direct comparison to literature is difficult, unless removal with exactly the same conditions were investigated. However, numerous studies have tested outdoor vegetation for NO2 removal, and this study aligns with those (Morikawa et al. 1998; Nowak et al. 2014; Petit et al. 2019) in the fact that all studied plant types were able to remove NO2 to varying degrees at a wide variety of concentrations. [SEP] Justify:

Output: The text discusses the ability of plants to remove nitrogen dioxide (NO2) from indoor environments under typical light conditions, but it does not mention air purifiers or HEPA filters specifically. Therefore, the text is irrelevant to the claim regarding air purifiers, leaving it with no supporting or contrasting information.


Input: Task: pass the claim using the given text. Claim: Many traditional medicines are derived from plants used by indigenous cultures. [SEP] Text: Hepatic cells (hepatocytes) were polygonal, contained large blue nuclei (sometimes binucleated) and granular pinkish cytoplasm. They were surrounded with well preserved cell membranes. Bile canaliculi between cell membranes were present. Hepatocytes were arranged in laminae which are plates of cells radiating away from central vein. Hepatocytes surrounded endothelial-lined hepatic sinusoids that contained fixed macrophages (Kupffer cells). Sinusoids drain blood into the central vein. At the periphery of hepatic lobules was the interlobular septum with portal triad, the latter contained bile duct; branches of hepatic artery and hepatic portal vein (Figure 1-5). Figure 1 Light micrograph of a section of normal female adult rat liver stained with H&E. The central vein (C) is present in the centre of the hepatic lobule. Hepatocytes (arrow) are polygonal, contain bluish nuclei, pinkish cytoplasm and have well preserved cell membranes. They are arranged into plates that radiate away from the central vein toward the periphery of the lobule. Hepatic sinusoids (arrow head) are located between the hepatic plates. Fixed macrophages (Kupffer cells) are present in sinusoid walls (M) X 200. Figure 2 Light micrograph of a section taken from normal adult rat liver stained with H&E. The central vein (C) is present in the centre of the hepatic lobule. Polygonal hepatocytes (arrow) are arranged in plates that radiate away from the central vein. They contain round bluish nuclei which may occasionally be binucleated (B), they have granular pinkish cytoplasm. Sinusoids (arrow head) are situated between plates of hepatic cells X 1000. Figure 3 Light micrograph of a section of control diabetic female adult rat liver stained with H&E. Normal histological features are present. The central vein (C) is in the middle of the hepatic lobule. Hepatocytes (arrow) are polygonal, contain bluish nuclei, pinkish granular cytoplasm and are surrounded by well preserved cell membranes. They are arranged into plates that radiate away from the central vein and surround the hepatic sinusoids (arrow head). [SEP] Justify:

Output: The text is irrelevant to the claim as it provides a detailed description of liver histology and cellular structures in rats, without any reference or connection to traditional medicines or their origins in indigenous cultures.


Input: Task: corroborate the claim using the given text. Claim: Some wearable devices use heart rate and movement data to estimate sleep stages. [SEP] Text: Strengths and LimitationsOur study was fully balanced in sex distribution and we were able to evaluate the Firstbeat method across two different nights in two different settings in the participants’ own homes. Thus, the ecological validity in this study can be considered excellent. As a limitation, even though our sample had some variation in PSQI-measured sleep quality, this study did not include any participants with diagnosed sleep disorders. Our study included only healthy participants, and the results are likely to be different if any health issues, particularly cardiovascular, or any sleep disorders are present. This is a question to solve before utilizing the Firstbeat method in clinical contexts. ConclusionCombining HRV with accelerometer measurements can be considered a feasible method with sufficient validity to measure nocturnal sleep stage variation. We found that the specificity, sensitivity, and accuracy were the weakest in detecting light sleep. Nevertheless, considering its availability, affordability, and ease of administration, Firstbeat may be a useful tool in various contexts, particularly in consumer-based sleep-measuring environments to produce an overview of sleep structures. 2012. . Normal human sleep: an overview. In: editors. Principles and practice of sleep medicine. Philadelphia: Elsevier Saunders; 2005:4. . Facial muscle tonus during REM and NREM sleep. Psychophysiology 1974 Jul;11(4):497-508. [CrossRef] [Medline]ély AA. A quantitative analysis of phasic and tonic submental EMG activity in human sleep. 1990 Nov;48(5):741-748. [CrossRef] [Medline]Parmeggiani PL. Physiologic regulation in sleep. In: editors. Principles and Practice of Sleep Medicine. Philadelphia: Elsevier Saunders; 2005:185. Van de. Effects of wake and sleep stages on the 24-h autonomic control of blood pressure and heart rate in recumbent men. Physiol 1994 Feb;266(2 Pt 2):H548-H554. [CrossRef] [Medline]. Sympathetic-nerve activity during sleep in normal subjects. N Med 1993 Feb 04;328(5):303-307. [CrossRef] [Medline]Szymusiak R. Body temperature and sleep. In: Romanovsky AA, editor. Handbook of Clinical Neurology. Philadelphia: Elsevier; 2018:341-351. . [SEP] Justify:

Output: The text corroborates the claim by mentioning that 'Combining HRV with accelerometer measurements can be considered a feasible method with sufficient validity to measure nocturnal sleep stage variation.' This implies that wearable devices, which typically utilize heart rate variability (HRV) and movement data collected by accelerometers, can indeed be used to estimate sleep stages, aligning directly with the assertion in the claim.


Input: Task: pass the claim using the given text. Claim: Air purifiers with HEPA filters can remove airborne pollutants indoors. [SEP] Text: e. 28. 3 and 18. 9 m3 h−1 m−2 green wall area, per different plant type) (Torpy et al. 2018). These however require a significantly increased amount of energy, irrigation considerations and thorough maintenance in comparison to simple potted plants and may not be suitable for all environments—albeit whilst providing greater removal (Cummings and Waring 2020). Thus, passive low-tech potted plant NO2 removal should not be overlooked given the wide and immediate availability across the globe and a significant potential to improve indoor air quality (for a review of research on indoor pollutant removal services of houseplants, see Gubb et al. 2020), particularly, in small offices in urban environments without smart, active ventilation systems. For occupants of these offices, a realistic number of potted plants may have comparable health benefits to clean air policies that are obviously crucial to reduce NO2 exposure outdoors. ConclusionThis study investigated if a simple set up, with just a potted houseplant, could be effective at passively removing the harmful pollutant NO2. This was carried out by investigating the ability of the combination of the plant species Spathiphyllum wallisii ‘Verdi’, Dracaena fragrans ‘Golden Coast’ and Zamioculcas zamiifolia with two different growing media to remove in situ concentrations of NO2 in real-time at two typical indoor light levels and in ‘wet’ and ‘dry’ growing media conditions. All studied growing medium–plant combinations were able to reduce the NO2 concentrations representative of a polluted urban environment, but to varying degrees. Growing medium and/or moisture contained within emerged as a significant pathway of NO2 removal. The greatest NO2 removal measured in a 150-L chamber over 1-h period corresponded to 272 ppb removed after 1 h per m2 of leaf area and 58 to 62 ppb per plant over the 1-h period for each of the three plant types in ‘wet’ growing media at ~ 500 lx. This would correspond to a removal of up to 3 ppb per m2 of leaf area after 1 h and 0. 62 ppb per plant after 1 h in a small office when not considering natural or mechanical ventilation. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the effectiveness of potted plants in removing NO2 indoor but does not mention HEPA filters or air purifiers. The focus on passive plant-based methods contrasts with the claim about mechanical filtration systems.


Input: Task: pass the claim using the given text. Claim: Many traditional medicines are derived from plants used by indigenous cultures. [SEP] Text: Portal triad with branches of hepatic portal vein (P) is seen X 200. Figure 4 Light micrograph of a section taken from control diabetic rat liver stained with H&E. Section shows normal histological features. The central vein (C) is located in the centre of the hepatic lobule and contains red blood cells (R), it is lined with endothelial cells (E). Polygonal hepatocytes (arrow) form plates which radiate away from the central vein. Hepatocytes contain bluish nuclei and granular acidophilic cytoplasm, bile canaliculi (BC) is visible between the cell membranes of the hepatocytes. Sinusoids (arrow head) are located between hepatocytes and drain into the central vein. Fixed macrophage (M) to the wall of one of the sinusoids is seen X 1000. Figure 5 Light micrograph of a section taken from the liver of non diabetic rat treated with vitamin E and stained with H&E. Section shows normal histological criteria. Central vein (C) is seen in the centre of the hepatic lobule. Hepatocytes (arrow) contain bluish nuclei and granular acidophilic cytoplasm, they are arranged in plates radiating away from the central vein. Sinusoids (arrow head) are located between cellular plates and drain into the central vein. Some macrophages (M) are located inside the sinusoids being fixed to their walls. X 400. S. polyanthum treated non diabetic rats: Non diabetic animals given S. polyanthum extract showed marked diffuse toxic destruction of the hepatocytes. Damage occurred throughout the hepatic lobules. Most cells were swollen. All forms of damaged nuclei were noticed (small, condensed and fragmented). Some hepatocytes had lost their nuclei. Cytoplasm was granular but sometimes, homogeneous. It is acidophilic and vacuolated. Cell membranes were not clearly visible. Liver tissue was markedly invaded by macrophages. The damage is graded as of severe type (Figure 6). Figure 6 Light micrograph of section of liver taken from non diabetic animal treated with S. polyanthum and stained with H&E. Cell necrosis is marked throughout the hepatic lobule. Hepatocytes are swollen and contain acidophilic granular or homogenous cytoplasm. [SEP] Justify:

Output: The text does not address the claim regarding traditional medicines and their derivation from plants used by indigenous cultures. Instead, it provides a detailed description of liver histology and the effects of a specific plant extract on non-diabetic rats, which is unrelated to the claim about traditional medicine.


Input: Task: corroborate the claim using the given text. Claim: Some wearable devices use heart rate and movement data to estimate sleep stages. [SEP] Text: [CrossRef] [Medline] Screening for (From the ). Cardiol 2018 Oct 15;122(8):1339-1344. [CrossRef] [Medline] of Breathing and on Measures - a pilot trial. 2020 May 04;10(1):7427. [CrossRef] [Medline]de. Validity, potential clinical utility, and comparison of consumer and research-grade activity trackers in I: In-lab validation against polysomnography. J 2020 Feb;29(1):e12931. [CrossRef] [Medline]. Feeling validated yet? A scoping review of the use of consumer-targeted wearable and mobile technology to measure and improve sleep. Rev 2018 Aug;40:151-159 [FREE Full text] [CrossRef] [Medline]. Wearables and the medical revolution. 2018 Sep;15(5):429-448 [FREE Full text] [CrossRef] [Medline]. The wrist is not the brain: Estimation of sleep by clinical and consumer wearable actigraphy devices is impacted by multiple patient- and device-specific factors. J 2020 Feb;29(1):e12926. [CrossRef] [Medline]. Accuracy of wristband Fitbit models in assessing sleep: systematic review and meta-analysis. J Res 2019 Nov 28;21(11):e16273 [FREE Full text] [CrossRef] [Medline]de. Wearable sleep technology in clinical and research settings. 2019 Jul;51(7):1538-1557 [FREE Full text] [CrossRef] [Medline]. Detection of Based on in Healthy Adults. 2017 Jan;21(1):123-133. [CrossRef] [Medline] Assessing sleep architecture and continuity measures through the analysis of heart rate and wrist movement recordings in healthy subjects: comparison with results based on polysomnography. 2016 May;21:47-56 [FREE Full text] [CrossRef] [Medline]. A systematic review of the accuracy of sleep wearable devices for estimating sleep onset. Rev 2020 Feb;49:101227. [SEP] Justify:

Output: The text corroborates the claim by discussing the analysis of heart rate and wrist movement recordings in healthy subjects to assess sleep architecture and continuity measures. This indicates that wearable devices indeed utilize physiological data related to heart rate and movement to estimate sleep stages.


Input: Task: contrast the claim using the given text. Claim: Air purifiers with HEPA filters can remove airborne pollutants indoors. [SEP] Text: Many factors could affect indoor microbial composition, including human occupancy, occupant behavior, pets and outdoor environment15,16,17,18. The air purifier was mainly used to remove indoor PM, partly comprising microorganisms. Microbes in the air could be transmitted to the HEPA filter in the air purifier. Suzuki et al. (2003) collected swab samples from the air purifier filters in the houses of seven healthy children with varicella. They found the varicella-zoster virus was detected in 29% of filters in air purifiers by day 319. Furthermore, Maus et al. (2001) studied the effect of the filters on the viability of microbes. They reported microbes could keep alive for a long time in the filter20. Majchrzycka et al. (2016) proved that the survivability of microorganisms on filter materials depends on the amount of accumulated moisture and microorganism type21. For example, Staphylococcus aureus survived well on filter materials with particular condition21. Under the working condition, microbes in the HEPA filter could be inspired into the air. Gore et al. (2003) reported that the old HEPA-filter vacuum cleaner markedly increases the inspired cat allergen in operation compared with baseline22. Spores were also detected downstream of the filters after a longer period of conditioning when ventilation was restarted23. These all indicated the microbes in the HEPA filter could be an important exposure source. Noris et al. reported the vast difference between HVAC filter and surface dust in an exploratory investigation in Austin24. However, the components of PM were distinctly different around the world. Surface PARTiculate mcollecting filter samples worldwide showed the chemical components of PM2. 5 varied by more than an order of magnitude between sites25. Besides, one research involving 19 cities around the world showed up to 100-fold in both bacterial and fungal levels among cities26. So, the microbial communities in the air purifier are regional. Moreover, limited studies were carried out on the microbial community in HEPA filter in an air purifier, while most focused on microbes in HVAC filter24,27,28,29,30,31. Knowledge of microbes in HEPA filter in office was also lacked, except the concentration and the size distribution of microbes32. [SEP] Justify:

Output: The text contrasts the claim by discussing how microbes can survive in HEPA filters and may even be expelled back into the air, challenging the effectiveness of HEPA filters as effective means of removing airborne pollutants. It cites studies showing that 'microbes in the HEPA filter could be inspired into the air' and that 'spores were detected downstream of the filters,' suggesting that instead of cleaning the air, HEPA filters might serve as a source of indoor airborne contaminants.


Input: Task: pass the claim using the given text. Claim: Many traditional medicines are derived from plants used by indigenous cultures. [SEP] Text: Some dead cells have eosinophilic (pink) granular or homogenous cytoplasm and darkly stained nuclei (arrow). Some cells contain small (S) or fragmented (F) nuclei. Some cells have lost their nuclei (arrow head). Liver is infiltrated with large numbers of macrophages (M) X 400. S. polyanthum extract treated diabetic rats: Animals treated with S. polyanthum extract showed marked toxic destruction of the hepatocytes throughout the hepatic lobule. Most of the cells were swollen. Some cells contained small nuclei while others showed condensed and deeply stained nuclei. Some hepatocytes exhibited fragmentation and disappearance of their nuclei. Cytoplasm was granular but in some other cells, it was homogeneous and was pale acidophilic and vacuolated. Cell membranes were not clearly visible. Marked infiltration of the liver with macrophages was noticed. The damage was graded as of severe type (Figure 7 & 8). Figure 7 Light micrograph of a section of a diabetic female adult rat liver treated with S. polyanthum extract. Section is stained with H&E. Central vein (C) in the centre of hepatic lobule is seen. Most of hepatocytes are swollen (arrow). Some of them have small nuclei (arrow head), other cells show nuclear condensation (CO), fragmentation (F) or disappearance (D). Cytoplasm is acidophilic, granular and vacuolated (V) in some cells but is homogeneous in others. Cell membranes are not clearly visible. Macrophage (M) infiltration of the liver is obvious X 400. Figure 8 Light micrograph of a section of a diabetic female adult rat liver treated with S. polyanthum extract. H&E staining. Portal triad shows hepatic portal vein (P) which contains macrophages, some of which (arrow) are penetrating the wall of hepatic portal vein. Other macrophages (arrow head) are seen outside the vessel; they are invading the liver tissue and are spread between hepatocytes. Hepatic artery (A) and bile duct (B) are also seen. Hepatocytes form plates around portal triad. Some of them contain small (S) and others contain fragmented (F) nuclei. Cytoplasm is acidophilic and granular X1000. O. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the cellular effects of S. polyanthum extract on diabetic rats, detailing histopathological findings and degenerative changes in liver cells. It does not provide any information regarding the origins of traditional medicines or their connections to indigenous cultures.


Input: Task: pass the claim using the given text. Claim: Some wearable devices use heart rate and movement data to estimate sleep stages. [SEP] Text: Normal values have been reported for these percentages. In summary, about 50–60% of time is spent in light sleep, 15–20% of time is spent in deep sleep, 20–25% is spent in REM sleep, and 5% or less is spent in wakefulness (Carscadon and Dement, 2000). The number of arousal from sleep and the number of wake periods is counted in order to give an impression on sleep fragmentation. Sometimes the number of transitions between sleep stages are also counted. Apart from these values there exist no normative data. Sleep fragmentation is often described in simple words but not given in quantitative numbers. We wanted to derive a quantitative description that goes beyond the simple counting of sleep-stage transitions. In order to better characterize the dynamics of sleep-stage transitions, we produced statistics on the duration of the sleep stages. For our study, we chose data from a normative set of sleep recordings obtained in normal subjects and subjects with sleep apnea. These sleep recordings were evaluated by two independent sleep experts, which strictly followed the guidelines of Rechtschaffen and Kales. After that a third sleep expert reviewed the evaluations of the two experts. In the case the two experts obtained different classifications, the third expert had to decide which one to choose. In this manner, we arrived at the best possible sleep scoring for our subjects. In order to obtain a new quantitative description we applied methods of statistical physics to the sleep profiles (Lo et al, 2002). The duration of the joined sleep stages (defined as light, deep, and REM sleep combined) followed an exponential distribution with a characteristic time scale τ, In contrast, the duration of the short wake periods during sleep followed a completely different distribution (Figure 2). They showed a scale-free power-law distribution, Figure 2The distributions of the durations of wake and sleep phases are plotted in (a) double-logarithmic and (b) linear-logarithmic presentation. This makes the different underlying laws clear. Wake period durations follow a scale-free power-law distribution (a) characterized by the exponent α. Sleep period durations follow an exponential distribution (b) characterized by the slope τ. These sleep recordings from 197 subjects were obtained as part of the multicenter studyFull size imageIn general, the value of the power-law exponent α remains the same when the time scale or time resolution changes. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses sleep stages and their characteristics in a general context without mentioning wearable devices or the use of heart rate and movement data for estimating sleep stages. It focuses on the statistical analysis and classification of sleep stages, rather than any specific technology or device that would corroborate the claim.


Input: Task: contrast the claim using the given text. Claim: Air purifiers with HEPA filters can remove airborne pollutants indoors. [SEP] Text: Our study showed that the HEPA filters in air purifiers used for a long time were with a higher proportion of viable bacteria compared with the dust. Considering the significantly different bacterial communities and bacterial diversity between the filter and dust samples, HEPA filter in the air purifier should be recognized as a new ecological niche in indoor environment. Source tracking analysis explained the formation of the environment in the filter and dust. The important sources of bacteria were soil and human oral, indoor air and outdoor haze in the case of filter and dust, respectively. The number of unique OTUs in the filter samples was more than twice as much as that in the dust samples. The unique OTUs made up 17. 1% and 17. 6% of the total reads in the filter and dust samples, respectively. Our study suggested that air purifiers should be included in the evaluation of indoor environmental health risks. MethodsSite description and sample collectionTwelve office rooms were selected as the sampling rooms in the Institute of Science (ILAS) in Beijing, China. These rooms were used as administrative offices that were not related to laboratory and animal houses. These rooms were distributed in three different buildings, and each room was independent of each other. Each room had openable windows. Ventilation was carried out by opening windows in no haze day. All the rooms were covered with tiles. Different types of house dust samples are widely used to assess the health effects of indoor microbes representing airborne inhalation exposure. Although it was reported a better reproducibility of microbial determinations has been found for mattress dusts80,81, settled dust was suitable to study the microbes in the office room. Settled dust reflects the microbial composition of indoor air and responds similarly to environmental determinants36. 2016, two randomly different areas on these room floors, which were larger than 2 × 2 cm2 with little or no direct contact with the occupants, were disinfected with bromogeramine and marked as floor dust sampling sites, and a new HEPA filter was set in the air purifier in each of these rooms. The flow rate of the air purifier was 116 m3/h. The air purifiers were applied in haze day in which a 24-hour average concentration was greater than 75 μg/m3. The sampling rooms and the occupant numbers in each room are shown in S1. [SEP] Justify:

Output: The text contrasts the claim by discussing the findings of a study that highlights the presence of viable bacteria in HEPA filters used in air purifiers, suggesting that rather than simply removing pollutants, HEPA filters can become ecological niches for bacteria. The presence of a higher proportion of viable bacteria compared to dust indicates that air purifiers may not solely serve the function of removing airborne pollutants, as they may also contribute to indoor microbial diversity.


Input: Task: pass the claim using the given text. Claim: Many traditional medicines are derived from plants used by indigenous cultures. [SEP] Text: sumatrana treated non diabetic rats: The liver of these rats showed diffused damage of the hepatic cells throughout the hepatic lobules. Hepatocytes sustained marked necrosis. Most cells were swollen and contained destroyed nuclei and eosinophilic (pink) granular or homogenous cytoplasm. Infiltrating macrophages were seen around blood vessels, in sinusoids and between hepatic cells. The damage was graded as of severe type (Figure 9). Figure 9 Light micrograph of a section of a non diabetic female adult rat liver treated with O. sumatrana extract. H&E staining. Note that most hepatocytes are swollen. Some of them contain small nuclei (arrow), other cells show condensation (CO), fragmentation (F) or disappearance of their nuclei (D). Cytoplasm is granular or homogenous. Some dead cells (DC) contain homogenous eosinophilic (pinkish) cytoplasm and dark nuclei. Infiltration with macrophages (arrow head) is noticed around blood vessels in portal triad, inside sinusoids and in between hepatocytes. Portal triad shows a branch of hepatic artery (A), bile duct (B) and hepatic portal vein (P) X 400. Octomeles sumatrana treated diabetic rats: Animals treated with O. sumatrana showed diffuse toxic effect of the extract on their hepatocytes. Cellular necrosis was noticed throughout hepatic lobules. Most hepatocytes were swollen and showed damaged nuclei (small, condensed, fragmented or disappeared). Cytoplasm was granular or homogenous, it was acidophilic and sometimes pale. Cell membranes were preserved. Macrophages were seen around portal vein and in between liver cells. The damage is of severe type (Figure 10 & 11). Figure 10 Light micrograph of a section of diabetic female adult rat liver treated with O. sumatrana extract. H&E staining. Hepatocytes are swollen (arrow). Some have small nuclei (arrow head) others show condensation (CO), fragmentation (F) or disappearance (D) of their nuclei. Cytoplasm is granular but in some other cells, it is homogeneous. Cell membranes are preserved. [SEP] Justify:

Output: The text does not address the claim regarding the origins of traditional medicines derived from plants used by indigenous cultures. Instead, it focuses exclusively on the effects of the plant Octomeles sumatrana on the livers of non-diabetic and diabetic rats, discussing cellular damage and histopathological observations without any reference to cultural practices or the derivation of traditional medicines.