Input: Task: corroborate the claim using the given text. Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: Health & 8, 2022 Detecting dental diseases with AI dental image analysis Dentists often use panoramic dental radiography, capturing the entire mouth in a single image, to diagnose dental diseases. The manual examination of these complex images is time consuming and laborious. at the Institute of Science and Technology (KAIST), together with Donghyun Kim, Jeong, and from InVisionLab, has developed a deep-learning model for AI dental-image analysis which enables the automatic detection of dental diseases from panoramic X-ray images. In addition to dental anomalies, radiographic findings from such images can be used in the diagnosis of systemic diseases. (AI) is employed extensively in dentistry to identify both normal and abnormal structures and diagnose diseases from the numerous medical images collected during a clinical routine. Panoramic dental radiography, which captures the entire mouth in a single image, is often used to diagnose dental diseases and plan subsequent treatment. These images are complex, and their manual examination is time consuming and laborious. In addition to dental anomalies, radiographic findings from such images can be used in the diagnosis of systemic diseases like hypo- and hyperparathyroidism (abnormally low or high levels of parathyroid hormone which affects calcium levels in the blood) and osteoporosis (porous bone). Due to time constraints, dental clinicians may concentrate only on teeth with symptoms, so the ability to filter out healthy images would aid diagnosis and save time. Sangyeon Lee, a PhD candidate from the Department of Bio and Brain Engineering, Institute of Science and Technology (KAIST), has collaborated with Donghyun Kim, Jeong, and from InVisionLab, to develop a deep-learning model for panoramic X-ray image analysis that enables the automatic detection of dental diseases. Three major components of panoramic X-ray images. AI dental image analysis The researchers explain how previous studies have employed AI in dentistry and been successful in the detection of targeted oral diseases. Still, they can only target a small number of abnormal signs or diseases. These models use convolutional neural networks to classify images and detect objects. They have not, however, exploited the key benefit of panoramic dental radiography in that it encapsulates the entire mouth in a single image. This includes the upper and lower jaws, individual teeth, and surrounding tissues and structures. [SEP] Justify:

Output: The text corroborates the claim by discussing the development of a deep-learning model for AI dental-image analysis that facilitates the automatic detection of dental diseases using panoramic X-ray images. It emphasizes how AI is employed to analyze complex images, which aligns with the assertion that AI can aid in early disease detection through image analysis.


Input: Task: contrast the claim using the given text. Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: Based on comparisons between the modeled output and observations, we estimate a root mean squared error on the modeled urban temperature of 1. 0 °C (Methods). Fig. 2: Population weighted mean temperature. a,b, Population-weighted daily mean urban-canopy air temperature time series in three scenarios (a), and temperature difference from the baseline scenario (b). Full size imageFigures 3 and 4 show how the differences in temperature between the scenarios are distributed across the population of London. Averaged across June, July and August, the whole population of would experience a lower mean temperature in both the cool roof and RPV scenarios. In the RPV and cool roof scenarios, we observe reduced mean daily maximum temperature but little difference in mean daily minimum temperature; this contrasts with the nonurban scenario, which shows greatly reduced daily minimum temperature but little difference in daily maximum temperature. The spatial pattern (Fig. 3) is substantially different between the cool roof and nonurban scenarios, emphasizing the conceptual difference between minimizing the urban heat island intensity (that is, minimizing urban–rural nighttime differences) and mitigating heat (reducing the high temperatures to which the population are exposed)23. Fig. 3: Spatial distributions of mean urban-canopy air temperature in the scenarios. a–d, The mean temperature is shown for the baseline scenario (a), and differences from the baseline are shown for the other scenarios (b–d). Temperature differences from the baseline scenario are shown for the rooftop PV scenario (b), cool roof scenario (c) and nonurban scenario (d). Boundaries are from Office for licensed under the Licence v. 3. 0 ( Contains OS data © Crown copyright and database right 2024. Full size imageFig. 4: Percentage of the population experiencing temperature differences between scenarios in RPV, cool roof and nonurban scenarios, compared with the baseline scenario. The mean daily mean (top), daily minimum (middle) and daily maximum (bottom) temperatures are shown. Full size imageEstimated heat-related mortalityUsing the population-weighted daily mean temperature, we estimate how many deaths and how many years of life lost (YLL) would be attributable to heat in each scenario. YLL is calculated summing average remaining life expectancy by age group. When deaths or YLL are lower than in the baseline scenario, we interpret these impacts as being avoidable by use of the intervention in that scenario. [SEP] Justify:

Output: The text contrasts the claim by discussing the effectiveness of different temperature adjustment scenarios in urban settings, including 'cool roof' and rooftop PV scenarios. It points out that while these interventions can reduce mean daily maximum temperatures, they do not necessarily lead to the cooling of entire cities during heat waves. Instead, it highlights the difference between urban heat island intensity reduction and overall temperature mitigation. This implies that rooftop gardens alone may not suffice to cool entire cities effectively.


Input: Task: pass the claim using the given text. Claim: Shift work can negatively impact metabolic health. [SEP] Text: According to previous studies, a non-standard shift is defined as one that starts before 07:00 or after 14:00, involves rotation, or is frequently worked outside the standard shift period (07:00–18:00 h). Work within the standard shift period, but frequently requiring overtime of several hours, is not recognized as a non-standard shift [3, 4]. The diagnostic algorithm and questionnaire content for SWSD is based on the of Sleep Disorders-3 (ICSD-3) diagnostic criteria and questionnaires developed by Barger LK et al. [3, 24]. Erectile function and ejaculation timing were evaluated using the of (IIEF-5) scores and (PEDT) scores, respectively, classifying an IIEF-5 score ≤ 21 as erectile dysfunction and a PEDT score ≥ 11 as indicative of premature ejaculation. Anxiety Disorder-7 (GAD-7) and Questionnaire-9 (PHQ-9) scored for anxiety and depression with scores ≥ 5 suggesting the presence of anxiety or depression, respectively. Detailed variable names and the questionnaire are provided in 1. After collecting the data, we performed quality control on the questionnaire data based on the following inclusion and exclusion criteria to eliminate surveys that did not meet the standards. Inclusion criteria were: (1) males aged 18 to 60; (2) individuals with a stable spouse or girlfriend engaging in regular sexual activity; (3) no severe underlying medical conditions, such as serious spinal cord injury, paraplegia, or advanced malignant tumors. Exclusion criteria included: (1) persons with psychiatric anomalies; (2) respondents whose survey answers were contradictory or who provided incomplete data; (3) completion of surveys in under three minutes, which were considered to reflect insincerity in response. Statistical analysisData were processed and analyzed using R version 4. 3. 0 and SPSS version 25. 0 for Windows (). Continuous data conforming to a normal distribution were presented as mean ± standard deviation (SD) and analyzed using Student’s t-test or Mann–Whitney test for inter-group differences. Categorical variables were described using frequency counts and percentages and analyzed using Pearson’s chi-square test or Fisher’s exact test. [SEP] Justify:

Output: The text does not address the claim regarding the negative impact of shift work on metabolic health. Instead, it focuses on definitions, diagnostic criteria for sleep disorders, and methodological details of data collection related to various health metrics. As such, it does not provide any direct support or contradiction to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: Moreover, filtering out healthy images would reduce the number of images requiring manual examination and reduce the burden on dental clinicians. Lee and his collaborators selected 17 fine-grained dental anomalies that can be detected from panoramic dental images. Using modern AI-based computer-vision techniques, they developed a model that can be applied in clinical practices to detect these anomalies. A huge number of high-quality data sets is essential for machine learning, so the research team collected 22,999 panoramic dental images from 30 local dental clinics over a year-long period from July 2020 to July 2021. Each image in the dataset was manually examined and labelled by a dental-radiography expert who noted any significant features or anomalies present in the image. These major dental anomalies can affect both patients’ dental health and their quality of life. Dental anomalies The researchers describe how these major dental anomalies can affect both patients’ dental health and their quality of life. If ignored, they can have severe consequences, particularly if they are connected to systemic diseases. Early detection can prevent serious outcomes and provide markers for other systemic diseases. The 17 anomalies were grouped into four categories based on their clinical features and locations: carious lesions, calcifications, anomalies in the dental regions, and anomalies in the surrounding regions. Examples of labelled carious lesions: a) cervical caries or cervical abrasions, b) dental caries or coronal defects, c) proximal caries, and d) secondary caries. Carious lesions, where there is a visible breakdown or a hole in the tooth surface, are the most common issue observed in dentistry. Their prevention and early diagnosis are crucial as they can cause more serious problems if ignored. The team divided carious lesions into four groups for fine-grained diagnoses to facilitate their early detection, preventing their progression and the deterioration of dental tissues. Calcification describe the build-up of calcium on the enamel and gums. Its presence on panoramic dental radiographs is rare, but detection is vital to prevent any further progression. Anomalies in the dental area include dental disease features and abnormal forms of teeth that can cause symptoms, including bleeding, pain, and halitosis (chronic bad breath). The researchers chose to include six dental anomalies that are critical to dental health. [SEP] Justify:

Output: The text corroborates the claim by discussing how AI-based computer-vision techniques were utilized to detect dental anomalies from panoramic dental images. It emphasizes the importance of early detection of these anomalies and highlights how machine learning and high-quality datasets enhance the accuracy of AI in identifying potential issues, thus supporting the assertion that AI can aid in early disease detection through image analysis.


Input: Task: contrast the claim using the given text. Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: SummaryWe estimate that 786 (95% confidence interval (CI) 655–920) deaths in in June, July and August of 2018 were attributable to heat (Table 1). Of these deaths, we estimate that 96 (95% CI 85–107) (12%) could have been avoided by full adoption of RPV, or 249 (95% CI 219–277) (32%) by full adoption of cool roofs. Most deaths attributed to heat take place among people 75 years and older. Based on life tables, we estimate 9,810 (95% CI 7,620–12,020) YLL to heat in London during June–August 2018, of which 1,200 (95% CI 990–1,430) could have been avoided by RPV, or 3,100 (95% CI 2,520–3,650) by cool roofs. Based on modeled differences in attributable mortality in for June, July and August of 2018 only, we estimate that the monetized value of heat-related mortality avoidable by RPV is £237 million (95% CI £211–265 million), and by cool roofs is £615 million (95% CI £541–684 million), relative to a standard low-albedo roof scenario. Similarly, the monetized value of YLL avoidable by RPV is £108 million (95% CI £89–129 million), and by cool roofs is £280 million (95% CI £228–329 million). We estimate that 9. 5–20 TWh of electrical energy would be generated in the full coverage RPV scenario during the same period, worth between £550 million and £7. 3 billion. DiscussionIn this study we modeled the effect of RPV and cool roofs on summer temperatures in London. We found that either RPV or cool roofs would reduce mean summer outdoor air temperatures in London under similar conditions to June–August 2018, and could therefore reduce heat-related mortality, with cool roofs having a larger effect. The economic value of mortality avoided in both scenarios are large and are of a similar order of magnitude to the consumer price of the electricity that would be generated, suggesting that the health-related externalities could be an important part of the strategic and wider economic case for cool roofs and solar panels. Temperature changes are generally consistent with comparable studies. The albedo cooling effectiveness—defined as the temperature change per 0. [SEP] Justify:

Output: The text contrasts the claim by indicating that both RPV (roof photovoltaic systems) and cool roofs can reduce outdoor air temperatures, but it emphasizes that full adoption of cool roofs has a larger effect compared to RPV, thus implying that rooftop gardens alone may not be sufficient to cool cities completely. The data suggests that adopting alternative cooling strategies can have significant impacts on heat-related mortality, but it doesn't support the notion that rooftop gardens alone are enough for cooling entire cities.


Input: Task: pass the claim using the given text. Claim: Shift work can negatively impact metabolic health. [SEP] Text: 22, 95% CI 0. 62—1. 83, p < 0. 001; Model 3, β 1. 56, 95% CI 0. 74—2. 39, p < 0. 001). Similarly, men with SWSD had higher premature ejaculation diagnostic scores than those without SWSD (Model 1, β 5. 08, 95% CI 4. 20—5. 96, p < 0. 001; Model 2, β 4. 15, 95% CI 3. 19—5. 12, p < 0. 001; Model 3, β 4. 03, 95% CI 3. 09—4. 98, p < 0. 001) (as depicted in Table 5). Table 5 Assessment of the association between shift work, SWSD, and PEDT scores using multiple regression models controlling for confounding factorsFull size tablePerformance of prediction modelTo more precisely quantify the contributory impact of each risk factor on the onset of premature ejaculation, we integrated risk factors identified through multifactorial regression analysis with clinical realities and constructed a nomogram including variables such as daily sleep hours, daily work hours, erectile dysfunction, anxiety, depression, years of shift work, alcohol consumption, diabetes, and hyperlipidemia, for the prospective forecasting of premature ejaculation incidents. Clinicians may utilize this nomogram to assign risk scores to patients, thereby assessing their risk of developing premature ejaculation. The model has been validated and demonstrated commendable sensitivity and specificity (with an the Curve, AUC, of 0. 855 in the training set and 0. 727 in the validation set). Calibration and decision curves further substantiate the model’s reliability (Fig. 2). Fig. 2Nomogram construction and validation for predicting the risk of premature ejaculation. a A nomogram was developed to predict the risk of premature ejaculation onset. b, c Characteristic (ROC) curves illustrate good sensitivity and specificity of the predictive model with an area under the curve (AUC) of 0. 855 for the training set and 0. 783 for the validation set. d, e Calibration plots demonstrate high predictive accuracy, with close alignment between the actual and predicted probabilities of premature ejaculation. [SEP] Justify:

Output: The text does not address the claim directly. While it discusses the associations between shift work, shift work sleep disorder (SWSD), and other health outcomes such as premature ejaculation, it does not provide clear evidence or information regarding the impact of shift work on metabolic health specifically. Thus, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: These are external root absorption, impacted tooth, periapical radiolucency (radiographic changes that are often the result of inflammation), residual root, and supernumerary tooth. They can also be considered as diagnostic markers and anatomical factors to inform a patient’s future dental work. For example, panoramic dental images can be used to evaluate the risk of nerve injury prior to an extraction. Example image of automatic anomaly detection. Anomalies situated in surrounding regions outside the dental area are seldom connected with oral health. They indicate signs of inflammatory processes and can be markers for the diagnosis of other related diseases. For instance, the thickening of the mucous membrane in the maxillary sinus can be an indicator of conditions such as apical periodontitis (painful inflammation of the tissue surrounding teeth) and alveolar bone loss that occurs in the bones that support the teeth. AI can be employed to alleviate the burden on dental clinicians, reducing the number of images that require manual examination. Detecting anomalies The detection process is divided into four steps. The images collected from the dental clinics are in DICOM format. DICOM stands for and Communications in Medicine and is an internationally accepted standard format for viewing, storing, retrieving, and sharing medical images. The first phase, the DICOM converter, involves converting these images into PNG format. Next is the disease detection stage; here, anomalies are identified using a trained Network (Faster R-CNN) model. This is an object detection model that detects boxed regions in the image where there are high possibilities of anomalies. The position verifier stage entails filtering out any boxes that are not located within a predetermined dental region. The final step is the polygon shaper stage, where a prebuilt library, Detectron2, is used to narrow down the region of abnormal signs from a box shape to a polygon. This enables dental clinicians to obtain high-resolution information regarding the location and anomaly types, as well as the precise regions that show the anomalies’ features. Training, testing, and evaluation Lee and collaborators divided the 22,999 panoramic dental images into training, validation, and test datasets. The images from July 2020 to March 2021 were used for training this deep-learning model to recognise the 17 anomalies and validate the detection. The remaining datasets were used as a test set for evaluating the model. /Shutterstock. [SEP] Justify:

Output: The text corroborates the claim by describing how AI can be employed in healthcare, specifically in the context of dental imaging, to detect anomalies. It outlines a four-step process in which AI, notably using a Faster R-CNN model, identifies abnormalities in dental images that can indicate diseases. The text emphasizes that by employing AI for image analysis, dental clinicians can benefit from high-resolution information regarding anomalies, which aligns with the claim's assertion that AI facilitates early disease detection via image analysis.


Input: Task: contrast the claim using the given text. Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: AbstractThe widespread adoption of rooftop photovoltaic solar panels in urban environments presents a promising renewable energy solution but may also have unintended consequences on urban temperatures. This is primarily due to their lower albedo, which leads to increased heat absorption and enhanced thermal convection between the panels and the underlying roof surfaces. Here we show that, in Kolkata, city-wide installation of these rooftop photovoltaic solar panels could raise daytime temperatures by up to 1. 5 °C and potentially lower nighttime temperatures by up to 0. 6 °C. Our study also reveals that rooftop photovoltaic solar panels significantly alter urban surface energy budgets, near-surface meteorological fields, urban boundary layer dynamics and sea breeze circulations. Comparative analysis with cities, such as Sydney, Austin, Athens and Brussels, supports these findings, providing valuable insights for policymakers on managing large-scale solar panel installations. Understanding these effects is crucial for balancing the benefits of renewable energy with its potential impacts on urban climates. Similar content being viewed by others On the local warming potential of urban rooftop photovoltaic solar panels in cities 4. However, the effects of city-scale deployment of RPVSPs on the urban microclimate remain uncertain. RPVSP efficiency diminishes in high ambient temperatures and humidity, raising concerns about their environmental impact and influence on the urban thermal environment4. Recent studies suggest that city-scale RPVSP deployment might either decrease or increase urban temperatures5,6,7. For example, it has been observed that the deployment of RPVSP on a building not only curtailed greenhouse gas emissions but also reduced annual cooling loads8. Similar results have been observed in simulation-based studies and in the case of medium-to-large-scale RPVSP deployment9,10,11,12. For instance, in the Paris metropolitan area during summer, RPVSPs reduced the urban heat island effect by 0. 2 °C during the day and up to 0. 3 °C at night, resulting in energy savings for air conditioning of up to 12% (ref. 9). reduced summer maximum temperatures by up to 1 °C because the need to import energy is offset by local generation10. Similarly, the cooling effect with RPVSPs in was found to be up to 0. 2 °C (ref. 11). [SEP] Justify:

Output: The text contrasts the claim by providing evidence that rooftop photovoltaic solar panels (RPVSPs), while having potential benefits for energy savings, can also lead to increased urban temperatures. It mentions that the deployment of RPVSPs could raise daytime temperatures in Kolkata by up to 1.5 °C, which directly challenges the idea that rooftop gardens alone can effectively cool cities during heat waves. Furthermore, the text indicates that the effects of RPVSP deployment on urban microclimates are uncertain and may vary, suggesting that relying solely on these installations is not a guaranteed solution for urban cooling.


Input: Task: pass the claim using the given text. Claim: Shift work can negatively impact metabolic health. [SEP] Text: 9In order to assess detailed information on shift-work and potential confounders by interview, we restricted the final cohort to the 58,091 female members who were alive on 1st July 2001, free of breast cancer, and born between 1933 and 1970 and therefore under 70years of age at the time of interview between March 2002 and July 2005. In this restricted cohort, 310 cases of histological confirmed primary breast cancer (ICD-7: 170) were documented during the period July 2001–June 2003, 301 (97%) of which were in women who were still alive at the time of interview. The incidence density method10 was used to select randomly four live breast cancer-free controls per case from the restricted cohort, who were matched on year of birth (±1year) and interviewed in the same period as the cases. Individual information on lifetime occupational and non-occupational exposures was obtained in a structured telephone interview by trained interviewers. An invitation was sent to all study participants 1–2weeks before the potential interview, which stated that the purpose of the study was to elucidate environmental causes of breast cancer in Danish nurses, without mentioning any potential cause. In a prepaid return letter, the potential study participant could suggest an appropriate time for the interview (between 8:00 and 22:00) and give a telephone number (work or private) or refuse to participate with no further contact. If we did not receive the return letter within 2weeks, we sent a follow-up letter. If there was still no reaction, the woman’s private telephone number was retrieved from the computerised telephone directory, and an interview was suggested between 10:00 and 21:00 for potentially retired women (aged ⩾60) and between 17:00 and 21:00 for the remaining women. Up to 20 telephone calls at different times of the day were attempted during 3months before a subject was excluded (four cases and seven controls). In total, 24 cases (8%) and 104 (9%) controls refused to participate, yielding completed interviews for 267 patients and 1035 controls. [SEP] Justify:

Output: The text provided does not address the claim regarding the negative impacts of shift work on metabolic health. Instead, it discusses the methodology for a study related to breast cancer incidence among Danish nurses, focusing on participant recruitment and interview procedures without any reference to the effects of shift work on metabolic health.


Input: Task: corroborate the claim using the given text. Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: com The group used an intersection over union (IOU) value to evaluate their model’s performance in correctly detecting lesions. IOU is an evaluation metric that measures the accuracy of an object detector. In this case, the area identified by the dental radiography expert is compared with the box identified by the model. The extent of overlap of two boxes is calculated by dividing the area of their overlap by the area of their union. If the two shapes overlap perfectly, the IOU score is 1. The researchers ascertained that an IOU value of 0. 5 or more denotes a predicted box that has been correctly detected. The class-wise performance of the detection model was compared with the labels provided by the dental radiography expert and measured in terms of precision, sensitivity, and specificity. The precision and sensitivity scores vary according to the anomaly types, with scores that exceed or match those from previous studies. The model demonstrated very high specificity, more than 95% in most classes, showing that it can successfully detect abnormalities and filter out normal healthy images. This high specificity indicates that this artificial intelligence model can reduce the burden of examination for dentists as it successfully filters out the healthy images and allows clinicians to focus on those images with potential anomalies. Training a deep-learning model The researchers discuss how the quantity and quality of the data are key when using AI techniques. Training their deep-learning model to detect 17 types of anomalies involved accruing datasets with sufficient examples of each class of object to be detected. They created a system that collects panoramic dental images direct from the dental clinics for a year, and a dental radiography expert labelled them manually. This high-quality dataset is still accumulating and can be used for future research. The high-performing tool successfully detects anomalies and filters out healthy images. Its high sensitivity (approximately 0. 99) validates its suitability for use in real clinical practices and demonstrates how AI can be employed to alleviate the burden on dental clinicians, reducing the number of images that require manual examination. This deep-learning model encompasses a large area of panoramic radiography and will help dentists improve patients’ quality of life through early diagnosis and prevention of diseases. What motivated you to develop this deep-learning model? Many diseases can be detected in the early stages from the panoramic X-ray image, and these early detections can increase patients’ quality of life. [SEP] Justify:

Output: The text corroborates the claim by describing the use of a deep-learning model to detect anomalies in panoramic dental images. It highlights that this AI tool not only detects diseases early but also improves the quality of life for patients by allowing for early diagnosis and prevention of diseases. Phrases like 'many diseases can be detected in the early stages from the panoramic X-ray image' and 'this high-performing tool successfully detects anomalies' support the assertion that AI can assist in early disease detection through image analysis.


Input: Task: contrast the claim using the given text. Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: Further, in Phoenix, Tucson and lowered the 2 m air temperature compared with conventional roofs and resulted in 8–11% reduction in cooling energy demand12. However, studies suggesting an increase in urban microclimatic temperature have observed that RPVSPs on urban rooftops might elevate the temperature at building scale, causing discomfort in outdoor urban environments13,14. Similar findings appear from the simulation studies5,7,14,15. For instance, large-scale RPVSP deployment can increase outdoor temperatures up to 0. 5 °C measured by the universal thermal comfort index during heatwaves7. RPVSPs can induce a temperature rise of up to 1. 5 °C during the daytime due to less efficient heat storage and increased sensible heat flux (Qsensible) from both RPVSPs and roof surfaces15. systems can significantly elevate daytime city temperatures and marginally lower them at night, depending on the climate zone and building types14. In Sydney, city-scale RPVSP deployment at the local district scale increased daytime air temperature by 1. 5 °C while reducing it by 2. 7 °C at night5. While existing literature reports on the impact of RPVSP on the urban environment, most are based on in situ field experiments or building-scale simulations, lacking a comprehensive multicity-scale analysis. These studies also neglect convective heat transfer between the roof surface and the back of solar panels. Even most of the modeling studies with RPVSP were purely based on simulations and were not evaluated in any shape or form. Our study addresses these gaps by incorporating new parameterizations for RPVSPs, including convective heat transfer, resulting in more aligned results with other studies incorporating similar considerations. The presence of RPVSPs on urban buildings, particularly rooftops, can have complex effects on the local thermal environment. are installed on roofs, they absorb a significant amount of solar energy, converting some of it into electricity but also generating heat in the process. This heat is released into the surrounding air, leading to an increase in air temperature around the panels. Moreover, the elevated installation of RPVSP creates two hot surfaces: the top surface of the panels and the underside surface. As air flows over these RPVSPs, it picks up heat more efficiently than it would from typical building or ground surfaces. [SEP] Justify:

Output: The text contrasts the claim by indicating that while rooftop solar panels (RPVSPs) can provide some benefits such as reducing cooling energy demand, they can also raise local air temperatures during heat waves. It states that RPVSPs can increase outdoor temperatures by up to 1.5 °C due to heat generated from their surfaces, which suggests they do not effectively cool cities on their own. Additionally, it mentions complex effects on urban thermal environments, highlighting that RPVSPs can sometimes lead to discomfort in outdoor urban environments, rather than cool them.


Input: Task: pass the claim using the given text. Claim: Shift work can negatively impact metabolic health. [SEP] Text: The structured questionnaire included sections eliciting information on: years of schooling, occupational history (all jobs held for at least 1year, including detailed work schedules), tobacco smoking pattern (age at start of smoking, total duration, average numbers of cigarettes, cigarillos or cigars smoked per day), alcohol drinking pattern (consumption of beer, wine or spirits at 15–24, 25–49 and ⩾50years), physical activity during leisure time (average hours spent per week in light activity, e. g. housework, walking, biking, and in sport at 15–24, 25–49 and ⩾50years), and reproductive history (age at menarche and at menopause, regularity of menstruation, number of births, age at birth of first child, breastfeeding, use of contraceptives), use of hormone replacement therapy, occurrence of breast cancer in mother and/or sister, weight and height at age 20 and at the time of interview, significant weight fluctuations since age 20, hours of sleep on a normal day at the time of interview and 10years previously. Finally, in order to obtain some indication of differential recall bias, exposure to electromagnetic fields in each job was ascertained from answers to the question: ‘How many hours per week did you work less than 0. 5m from an electrical device, e. g. medical device, computer or electric machinery’2. 1 Shift-work definitionFor each job held for at least 1year, we assessed information on year or age of start and end, average number of hours worked per week, time of starting and ending on each shift in a normal week (excluding irregular overtime work). If the woman had worked in rotating shifts, we classified the job according to the most frequent occurring rotating shift system in Danish nurses: day–evening, day–night, day–evening-night or others. For most of the nurses, day work was from 6 or 7 to 15 or 16, evening work from 15 or 16 to 23 or 24 and night work from 23 or 24 to 7 or 8. [SEP] Justify:

Output: The text provides detailed information regarding a questionnaire used to gather data on various health and lifestyle factors, including shift work classifications for nurses. However, it does not present any information that clearly supports or contradicts the claim about the negative impact of shift work on metabolic health; thus, it is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: However, it takes too much time for dental clinicians to examine all newly taken images every day. We therefore decided to help both dental clinicians and patients using AI. By suggesting the possible regions of dental anomalies and normal images which don’t need further examination, the AI system can reduce the burden for clinicians so they can focus on their patients and treatments. Detecting dental diseases with AI dental image analysis was last modified: November 8th, 2023 Keywords: AIAI dental imagesdeep learningdental anomaliesdental diseasesdental radiographydentistsInstitute of Science and Technologyoral diseasesoral healthpanoramic X-ray imagesradiographyDownload this 5172. Behind the Lee is a PhD candidate in the Department of Bio and at Institute of Science and Technology (KAIST). Main research fields are bioinformatics and data science. of InVisionLab: Major in electrical and electronic engineering at Institute of Science and Technology (KAIST). He worked for ten years at medical service companies and became interested in the convergence of the medical industry. As a doctoral student at School of Dentistry, he studies the convergence of technology and dental medical service. Hogul Jeong, Head of research institute at InVisionLab: PhD in dentistry from College of Dentistry. With more than 20 years of experience in the dental imaging and diagnosis field, he is researching dental image diagnosis and artificial intelligence. Jaehyeong Park, Programmer at InVisionLab: Majoring in computer science and engineering at University, he works at InVisionLab as a programmer, developing front-end services and other applications. He is also a cellist at the Orchestra. Address: G114-S127, 128, Beobwon-ro, Songpa-gu, Seoul, Republic of Korea, 05854 E: [email protected] T: +82 10 6606 8885 Lee and his collaborators have developed a deep-learning model that can detect dental diseases from radiography images. Funding InVisionLab is a company developing automatic analysis and diagnosis of clinical dental images like panoramic X-ray, CBCT image diagnosis, and cephalometric images. Cite this 10. 32907/RO-130-2773295876. (2022). Detecting dental diseases with AI dental image analysis. Research Outreach, 130. Available at: This work is licensed under a Attribution-NonCommercial-NoDerivatives 4. [SEP] Justify:

Output: The text corroborates the claim by discussing how an AI system can assist dental clinicians in examining dental images. It mentions that the AI can suggest possible regions of dental anomalies, which implies it can aid in early disease detection through image analysis. The text specifically states that the developed deep-learning model is capable of detecting dental diseases from radiography images, aligning directly with the claim that AI can help in disease detection through image analysis.


Input: Task: contrast the claim using the given text. Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: Observational studies in the literature have shown that areas with RPVSP arrays can experience higher daytime air temperatures compared with reference sites without RPVSP. However, the effect on nighttime temperatures is generally minimal. Given these conflicting studies, it is imperative to comprehensively analyze the impact of city-scale RPVSP deployment in diverse cities globally. Our study focuses on the advanced capabilities of the latest weather research and forecasting (WRF) model integrated with multilayer urban schemes, such as a building energy model (BEM) coupled with building effect parameterization (BEP), that is, WRF/BEP + BEM, especially in tropical cities. This model accurately captures the energy balance of RPVSPs, an aspect overlooked in previous studies. To address this concern, we have validated the WRF/BEP + BEM-simulated RPVSP surface temperature against an experimentally validated model. at a city scale is crucial for sustainable energy solutions as it assesses broader impacts on urban microclimates and energy consumption patterns. Through city-wide implementation scenarios and simulation studies, we are suggesting improvements to RPVSP efficiency, which can lead to a decrease in convective heat generation. This is crucial because the efficiency of RPVSPs decreases by approximately 0. 5–0. 8% for each 1 °C rise above standard test conditions (25 °C)16. Our comprehensive assessment includes parameters such as surface energy budgets, near-surface meteorological fields, boundary layer dynamics and sea breeze circulations. The findings can inform policy discussions and lead to redesigning policies for RPVSP implementation in cities. By addressing overheating issues and advocating for advanced RPVSP technologies and efficient cooling strategies, we aim to promote sustainable urban development practices and enhance the understanding of RPVSP deployment’s broader impacts. ResultsRegional impacts on urban surface energy budgetsCity-scale deployment of RPVSPs led to changes in the Qsensible from urban surfaces. At 14:00 LT, Qsensible peaked at 415. 9 W m−2 for RPVSPs 25%, 469. 2 W m−2 for RPVSPs 50%, 533. 9 W m−2 for RPVSPs 75% and 595. 2 W m−2 for RPVSPs 100%. Similarly, at 18:00 LT, the average Qsensible values were 245. [SEP] Justify:

Output: The text contrasts the claim by indicating that areas with RPVSP (Rooftop Photovoltaic and Vegetation Surface Planter) systems can experience higher daytime air temperatures relative to reference sites without RPVSP. It emphasizes the need for a comprehensive analysis of RPVSP impacts on urban microclimates and highlights that RPVSP efficiency may decrease with rising temperatures, suggesting that rooftop gardens alone are insufficient for cooling entire cities during heat waves.


Input: Task: pass the claim using the given text. Claim: Shift work can negatively impact metabolic health. [SEP] Text: We used two series of partly overlapping variables to characterise the shift-work in each job held: type of shift (permanent day, rotating day–evening, day–night, day–evening–night, other rotating and permanent night), with calculated lifetime duration of each shift system weighted by hours worked per week; and number of shifts after midnight per month (‘graveyard shifts’, beginning after 19 and ending before 9), with calculated lifetime cumulative numbers of such shifts based on the duration and number per month, including tertiles based on the distribution in exposed controls. If subjects reported years of after midnight shifts, but were unable to provide numbers of such shifts per month (4. 7% of cases and 4. 8% of controls), we imputed this information by the average number for cases and controls, respectively. Overall, this changed final results only marginally. The date of birth of each child was obtained for each woman from the Registry in order to verify the self-reported numbers. 2. 2 Statistical analysesWe estimated relative risks as odds ratios (ORs) with 95% confidence intervals (CIs) by multiple logistic regression analyses. All the models were conditional on 5-year age groups. Univariate associations between breast cancer and each of the above-mentioned potential confounders were evaluated and included in the final models with the shift-work variables, for adjusted ORs, if the p value was ⩽0. 15 (Pearson chi-square test for categorical variables and t test for continuous variables) or if inclusion of a covariate changed the odds ratio in the model with shift-work by ⩾15%. The final models included, in addition to the shift-work variables, breast cancer in mother or sister, use of hormone replacement therapy (never, <5, ⩾5years), lifetime cumulative months of breastfeeding, age at first child birth (nulliparous, <25, 25–29, 25–30, ⩾35years), age at menarche (continuous), menstrual regularity, weight fluctuations and menstrual status. The reported significance levels are two-sided. 3 ResultsThe characteristics of the control women by after-midnight shift status are shown in Table 1. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the statistical methods used to analyze the relationship between shift work and breast cancer without addressing metabolic health specifically. The focus is on classifying types of shifts and conducting statistical analyses rather than on the impact of shift work on metabolic health.


Input: Task: corroborate the claim using the given text. Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: ViewpointScott1, Thompson2, 1Centre for Cancer Screening, Prevention and Early Diagnosis, of Population Health, University of London, London, United Kingdom2Department of Family Medicine, University of :for Cancer Screening, Prevention and Early Diagnosisof Population HealthUniversity of 1M 6BQUnited KingdomPhone: 44 2078823550Email: suzanne. scott@qmul. ac. ukAbstractThis viewpoint paper considers the authors’ perspectives on the potential role of smartphones, wearables, and other technologies in the diagnosis of cancer. We believe that these technologies could be valuable additions in the pursuit of early cancer diagnosis, as they offer solutions to the timely detection of signals or symptoms and monitoring of subtle changes in behavior that may otherwise be missed. In addition to signal detection, technologies could assist symptom interpretation and guide and facilitate access to health care. This paper aims to provide an overview of the scientific rationale as to why these technologies could be valuable for early cancer detection, as well as outline the next steps for research and development to drive investigation into the potential for smartphones and wearables in this context and optimize implementation. We draw attention to potential barriers to successful implementation, including the difficulty of the development of signals and sensors with sufficient utility and accuracy through robust research with the target group. There are regulatory challenges; the potential for innovations to exacerbate inequalities; and questions surrounding acceptability, uptake, and correct use by the intended target group and health care practitioners. Finally, there is potential for unintended consequences on individuals and health care services including unnecessary anxiety, increased symptom burden, overinvestigation, and inappropriate use of health care resources. JMIR Cancer 2024;10:e52577doi:10. 2196/52577Keywordswearables; early diagnosis; cancer; challenges; diagnosis; wearable; detect; detection; smartphone; cancer diagnosis; symptoms; monitoring; monitor; implementation; anxiety; health care service; mobile phone IntroductionThere is growing use of smartphones, wearables, and other technologies in health and wellness, either as consumer products or medical devices. (NHS) Plan [1] anticipates that in 10 years, people will have “the option for their physiology to be effortlessly monitored by wearable devices. People will be helped to stay well, to recognize important symptoms early, and to manage their own health, guided by digital tools. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of technologies such as smartphones and wearables in the timely detection of signals or symptoms related to cancer. It asserts that these technologies can enhance early cancer diagnosis by monitoring subtle changes that may be overlooked. The mention of recognizing important symptoms early aligns with the claim that AI can assist in early disease detection through advanced analytical capabilities.


Input: Task: contrast the claim using the given text. Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: 6 W m−2, 271 W m−2, 285. 4 W m−2 and 306. 6 W m−2 for RPVSPs 25%, RPVSPs 50%, RPVSPs 75% and RPVSPs 100%, respectively. This significant rise was primarily attributed to RPVSPs’ low albedo value, leading to amplified solar energy absorption, predominantly emitted as sensible heat rather than converted into usable electricity. During the early morning and nighttime, minor fluctuations in Qsensible were observed, revealing the intricate interplay of RPVSPs with the urban heat environment. Furthermore, the latent heat flux (Qlatent) from the urban surface exhibited a corresponding increase owing to the energy mechanisms of the RPVSPs array. At 14:00 LT, the peak Qlatent reached 100 W m−2 for RPVSPs 25%, 107. 8 W m−2 for RPVSPs 50%, 113 W m−2 for RPVSPs 75% and 121. 5 W m−2 for RPVSPs 100%. Likewise, at 18:00 LT, the average Qlatent ranged from 66. 3 W m−2 to 80. 3 W m−2 for RPVSPs 25% to RPVSPs 100%, highlighting the increased Qlatent resulting from RPVSP installation. These alterations were associated with a marginal temperature rise surrounding the RPVSPs, potentially influencing evaporation and significantly contributing to the Qlatent of urban areas. Moreover, the installation of RPVSPs over building roof surfaces led to augmented ground heat storage (Qstorage) on urban surfaces. In the early morning, the average Qstorage ranged from 53. 5 W m−2 to 56. 6 W m−2 for RPVSPs 25% to RPVSPs 100%, respectively. The maximum Qstorage during peak hours ranged from −270. 2 W m−2 to −310. 8 W m−2 for RPVSPs 25% to RPVSPs 100%, with the maximum increase during this time ranging from 18. 9 W m−2 to 59. 5 W m−2. The convention taken is that the surface energy fluxes are counted positive when they are upward (relative to the surface). Diurnal changes in Qstorage showed an average increase during the daytime, ranging from 9. 9 W m−2 to 32. [SEP] Justify:

Output: The text contrasts the claim by discussing the energy mechanisms of RPVSPs (Rooftop Photovoltaic and Solar Plants) and their impact on urban heat environments. It indicates that the installation of RPVSPs led to both sensible and latent heat flux changes, which may contribute to temperature rises rather than cooling. The details provided show how energy absorption and heat storage occur, suggesting that rooftop gardens alone may not effectively cool entire cities during heat waves.


Input: Task: pass the claim using the given text. Claim: Shift work can negatively impact metabolic health. [SEP] Text: Nurses who worked on after-midnight ‘graveyard’ shifts, in comparison with nurses on day shifts, had a significantly longer working life, were younger at menarche and menopause, had fewer children, were older at the birth of their first child, spent fewer hours weekly on sport, had slept fewer hours per night 10years before the interview, and were more frequently exposed to electromagnetic fields. No other significant differences were found. Table 2 shows the lifetime work schedule characteristics of cases and controls. The median length of work was 25. 8years for cases and 24. 1years for controls. Permanent day work constituted on average over half of the working time, and about 7% of cases and 5% of controls had ever had permanent night shifts for at least 1year. Day–evening–night shift-work, the most frequent rotating shift system, had been experienced by 81% of controls and 71% of cases, constituting 32% of the work time in cases and 31% in controls. Rotating day–evening, day–night, and other shifts constituted only 13% of the total work time in both cases and controls. No cases or controls had permanent night work for the entire work history. CharacteristicAfter-midnight shiftap ValuebNever (<1year) (n=252)Ever (⩾1year) (n=783)n%n%Age (years)<4025105470. 0940–442610841145–4943171401850–5437151772355–5950201321760–64371511615⩾6534138010 Length of work (cumulative years)1–513551<0. 0015–10341435510–1531121061415–2029121171520–2546181441825–30461812416⩾30532125231 Marital statusMarried19477563720. 21Unmarried1568110Divorced2498711Widow208506 Length of schooling (years)726106580. 548–10973833343⩾101295138449 Body mass index mean, kg/m2 (SD)At time of interview25221. 3 (3. 3)78321. 3 (2. 5)0. 99At age 20years25123. [SEP] Justify:

Output: The text discusses various demographic and work schedule characteristics of nurses working night shifts compared to day shifts, but does not provide direct evidence or conclusions regarding the impact of shift work on metabolic health. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: ” Similarly, in 2020, the US Food and (FDA) launched a Plan to encourage digital health innovation as “digital health technologies can empower consumers to make better-informed decisions about their own health and provide new options for facilitating prevention, early diagnosis of life-threatening diseases, and management of chronic conditions outside of traditional care settings” [2]. Wearables are devices that can be worn to detect and monitor biometric data such as heart rate, blood oxygen saturation, sleep pattern, or temperature while the wearer continues their normal routines. A further category of wearables involves skin patches used to measure biochemical signals (ie, glucose) on a continuous basis that are increasingly being considered as a standard of care for individuals with certain conditions (eg, diabetes) [3]. While most wearables have been wrist-worn devices, similar physiologic signals are now being generated from other devices such as rings or earbuds. Smartphones are also increasingly being used for health and wellness; they have the advantage of far higher use (compared with wearables), and a growing array of different sensors are routinely embedded. Could smartphones and wearables help detect cancer and, importantly, detect cancer earlier in its disease course when it is more likely to be localized and with a better prognosis? This paper provides an overview of the scientific rationale as to why these technologies could be valuable for early detection of cancer, the potential barriers to successful implementation, and the next steps for research and development. Potential of Smartphones and Wearables for of CancerWhile national cancer screening programs offer the opportunity to detect cancer or precancerous lesions in asymptomatic individuals, routine screening currently only accounts for the minority (<10%) of cancer diagnoses [4,5]. The predominant route to a cancer diagnosis is symptomatic presentation to ambulatory care. Thus, the diagnosis of cancer heavily relies on patients’ ability to notice and attend to relevant bodily changes and their decision to consult a health care professional [6,7]. However, noticing relevant bodily changes is challenging given the multiple subtle changes that may signal cancer among the plethora of daily bodily changes; fluctuations of normal bodily processes; self-limiting, transient symptoms; and the presence of chronic conditions. The signal-to-noise ratio is weak. This issue is exacerbated by individuals’ limited ability to accurately interpret vague bodily changes, many of which can be associated with cancer (eg, fatigue, weight loss, and stomach upset). [SEP] Justify:

Output: The text corroborates the claim by discussing how digital health technologies, including wearables and smartphones, are implemented for the early diagnosis of diseases such as cancer. It mentions that these technologies could be valuable for early detection, specifically when addressing the potential for detecting cancer earlier in its disease course, supporting the idea that AI and image analysis can enhance disease detection.


Input: Task: contrast the claim using the given text. Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: 8 W m−2 for RPVSPs 25% to RPVSPs 100%, and during nighttime, the average increase ranged from 4. 9 W m−2 to 10. 4 W m−2 for all RPVSP scenarios. This is due to the additional radiation and heat transfer between the RPVSP and roof surface beneath the panel. Additionally, the widespread deployment of RPVSPs at a city scale altered the surface energy balance on the urban surface by transferring heat from RPVSPs to the urban atmosphere, as evidenced by changes in net radiation (Qnet) (Fig. 1). This describes the total radiation emitted to the atmosphere, including the radiation (longwave) from RPVSPs to the urban atmosphere. Early morning average Qnet ranged from −29. 2 W m−2 to −8. 1 W m−2 for RPVSPs 25% to RPVSPs 100%, while the peak Qnet varied from 786. 1 W m−2 to 1,027. 5 W m−2. This enhanced Qnet during peak hours ranged from 94 W m−2 to 335. 4 W m−2 over the urban surface. Furthermore, the daytime maximum increase of Qnet ranged from 51. 9 W m−2 to 193. 5 W m−2, with a slight nighttime increase ranging from 10 W m−2 to 31. 9 W m−2 for all RPVSP scenarios. These findings underscored the multifaceted impact of RPVSPs on the surface energy balance of urban areas. Fig. 1: Changes in simulated Qnet. a–d, The changes (building rooftops with RPVSPs scenarios minus building rooftops without RPVSPs scenarios) in simulated Qnet during daytime (at 14:00 LT, top) and nighttime (at 02:00 LT, bottom) for RPVSPs 25% (a), RPVSPs 50% (b), RPVSPs 75% (c) and RPVSPs 100% (d) over an urban grid cell. The urban area of Kolkata is marked by the black line inside the box. CTRL refers to control case. [SEP] Justify:

Output: The text contrasts the claim by detailing the effects of RPVSPs (Rooftop Photovoltaic-Solar Panels) on the urban microclimate and energy balance, indicating that their operation alters the surface energy balance in urban areas. While it discusses radiation and heat transfer associated with these systems, it does not support the notion that rooftop gardens alone can cool cities, as it highlights the multifaceted impacts of RPVSPs rather than attributing cooling effects purely to rooftop gardens.


Input: Task: pass the claim using the given text. Claim: Shift work can negatively impact metabolic health. [SEP] Text: 9 (4. 7)78224. 1 (3. 5)0. 58 Significant weight fluctations since age of 20yearsNo18473537690. 08Yes672624631 Age at menarche (years)⩽126024158200. 031260242673413642516922⩾14672718524 Menstrual regularityYes22489719920. 15No2811648 Menopausal status at diagnosisPre11244361460. 22Post1405642254Age at menopause (years±SD)16650. 2 (6. 5)50348. 9 (5. 2)0. 03No menopause8634280360. 11<408339540–4423960845–4968272112750–54441815620⩾55239375 No. of children026108911<0. 00112081121428433329423922719425⩾33112597Age at birth of first child (years±SD)22724. 9 (3. 7)69426. 6 (4. 2)<0. 001Cumulative length of breastfeeding (months±SD)22715. 4 (13. 9)69413. 9 (13. 0)0. 15 Use of oral contraceptivesNever6626233300. 12Yes1857355070Mean duration (years±SD)1846. 8 (6. 1)5466. 2 (6. 8)0. 32 Use of hormone replacement therapyNever17067491630. 35Yes813229037Mean duration (years±SD)766. 1 (6. 2)2736. 4 (5. 9)0. 75 Breast cancer in mother or sisterNo23192727930. 50Yes177506 Physical activity at work (weighted average over time)Light291277100. 46Some562219625Heavy1054234644Very heavy622516421Sport activities, weighted weekly average hours (SD)2204. 2 (5. 3)7422. 8 (3. 1)<0. 001 Sleep during average day off 10years before interview (h)<6734960. [SEP] Justify:

Output: The text does not provide any information that directly supports or contradicts the claim regarding the negative impacts of shift work on metabolic health. It appears to contain statistical data and variables related to health but lacks context or a direct mention of 'shift work' or its implications for metabolic health. Therefore, it is deemed irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: This is because our awareness, attention, and interpretation are affected by expectations; emotions; beliefs; and biological, environmental, sociodemographic, and contextual factors [8-11]. Furthermore, symptoms may evolve very slowly over time, making it difficult to notice subtle changes. It is reported that the predominant risk factor for delay in seeking help following the detection of cancer symptoms is the “lack of interpretation by patients of the serious nature of their symptoms” [12]. Smartphones and wearable technologies have the potential to facilitate the detection and tracking of bodily changes that might otherwise be dismissed or interpreted as only needing self-medication rather than the attention of a health care professional. There is emerging data about early, subtle signs of cancer, and some of these may be amenable to detection by electronic sensors and monitoring of behavior (see Table 1). Table 1. Potential signals of cancer that can be measured using sensors in smartphones or wearables. Sensors currently available on some smartphones or wearablesExamples of signals for health features that could be related to cancerAudio signals from microphones [13]Changes in cough and breathing difficulty (associated with lung cancer) [14]Changes in voice such as hoarseness (associated with head and neck cancer and lung cancer) [14]GPS location tracking and activity tracking [15]Reduced activity resulting from fatigue (associated with multiple cancers) [14]Image capture and analysis [16-18]Anemia detected from images of the skin or eyes (associated with multiple cancers) [19]Jaundice detected from images of the skin or eyes (associated with pancreatic cancer) [20]Changes in skin lesions (associated with skin cancer) [14]Temperature measurement [21]Rise in temperature (associated with pancreatic cancer) [22]Body composition using image analysis and electro dermal activity [23]Weight loss (associated with multiple cancers) [14]Photoplethysmogram [24]Anemia (associated with multiple cancers) [19]Sensors could allow the detection of changes prior to them being noticed or interpreted as symptoms, for example, a reduction in activity prior to fatigue or changes in food consumption prior to weight loss. There is recent evidence that monitoring day-to-day purchases could detect an increase in over-the-counter pain and indigestion medication 8 months prior to ovarian cancer diagnosis [25]. [SEP] Justify:

Output: The text corroborates the claim by discussing various technologies, including image analysis, that can potentially facilitate the early detection of diseases, particularly cancer. It mentions how image capture and analysis can reveal symptoms such as anemia or jaundice when viewing changes in skin or eyes, indicating that AI-assisted image analysis can be an effective tool in detecting these diseases early.


Input: Task: contrast the claim using the given text. Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: The increase in the 2 m Tambient and Tsurface during the daytime can be attributed to the heat absorption capacity of the RPVSPs and the subsequent release of stored heat during the nighttime, leading to a decrease in surface temperatures. The changes in Hrelative were influenced by alterations in atmospheric stability, induced by the modified energy balance due to RPVSP deployment. Furthermore, the shifts in Wspeed were influenced by the changes in the surface temperature and subsequent alterations in boundary layer mixing induced by RPVSP deployment. These findings stress the importance of evaluating RPVSPs’ impact on urban microclimates, especially in hot and humid regions. It highlights the need for effective mitigation strategies and emphasizes the significance of RPVSPs’ thermal properties in future urban planning and climate-sensitive designs. Fig. 2: Changes in simulated Tambient. a–d, The changes (building rooftops with RPVSPs scenarios minus building rooftops without RPVSPs scenarios) in simulated 2 m Tambient during daytime (at 14:00 LT, top) and nighttime (at 02:00 LT, bottom) for RPVSPs 25% (a), RPVSPs 50% (b), RPVSPs 75% (c) and RPVSPs 100% (d) over an urban grid cell. The urban area of Kolkata is marked by the black line inside the box. Full size imageFig. 3: Changes in simulated Tsurface. a–d, The changes (building rooftops with RPVSPs scenarios minus building rooftops without RPVSPs scenarios) in simulated Tsurface during daytime (at 14:00 LT, top) and nighttime (at 02:00 LT, bottom) for RPVSPs 25% (a), RPVSPs 50% (b), RPVSPs 75% (c) and RPVSPs 100% (d) over an urban grid cell. The urban area of Kolkata is marked by the black line inside the box. Full size imageRegional impacts on urban boundary layer dynamicsThe combined effects of rapid urbanization and regional climate change-induced warming are expected to intensify heatwave events, impacting the planetary boundary layer (PBlayer) height in cities. The simulation demonstrates that widespread RPVSP deployment alters the lower atmospheric PBlayer structure over urbanized areas, such as Kolkata, especially during daytime (Supplementary Fig. [SEP] Justify:

Output: The text contrasts the claim by discussing the thermal effects of RPVSPs (Rooftop Vegetation-Promoting Surface Properties) rather than solely rooftop gardens. It indicates that while RPVSPs can influence urban microclimates, the cooling effect depends on various factors like the urban boundary layer dynamics and energy balance, not just the deployment of rooftop gardens alone. This suggests that the claim oversimplifies the role of rooftop gardens in addressing heat waves.


Input: Task: pass the claim using the given text. Claim: Shift work can negatively impact metabolic health. [SEP] Text: Biological clocks and shift work: circadian dysregulation and potential long-term effectsControl. 2006; 17:489-500CrossrefScopus (315)PubMed3. Stevens, R. G. ∙ Hansen, J. ∙ Costa, G. . . . Considerations of circadian impact for defining ‘shift work’ in cancer studies:. 2001; 68:154-162CrossrefScopus (324)4. Straif, K. ∙ Baan, R. ∙ Grosse, Y. . . . Carcinogenicity of shiftwork, painting, and firefightingLancet Oncol. 2007; 8:1065-1066Full Text(PDF)PubMed5. Hansen, J. Light at night, shiftwork, and breast cancer riskJ Inst. 2001; 93:1513-1515CrossrefScopus (141)PubMed6. Costa, G. ∙ Haus, E. ∙ Stevens, R. Shift work and cancer – considerations on rationale, mechanisms, and epidemiologyHealth. 2010; 36:163-179CrossrefScopus (208)PubMed7. Kjaer, T. K. ∙ Hansen, J. Cancer incidence among a large cohort of female Danish registered nursesHealth. 2009; 35:446-453CrossrefScopus (27)PubMed8. Pedersen, C. B. systemSuppl. 2011; 39:22-25CrossrefScopus (3295)9. Gjerstorff, M. L. . 2011; 39:42-45CrossrefScopus (1078)10. Richardson, D. B. An incidence density sampling program for nested case–control analysesMed. 2004; 61:e59CrossrefScopus (186)PubMed11. Stevens, R. G. Electric light causes cancer? Surely you’re joking, Mr. StevensMutat Res. 2009; 682:1-6CrossrefScopus (35)PubMed12. Gooley, J. J. ∙ Chamberlain, K. ∙ Smith, K. A. . . . Exposure to room light before bedtime suppresses melatonin onset and shortens melatonin duration in humansJ Metab. 2011; 96:E463-E472CrossrefScopus (367)PubMed13. [SEP] Justify:

Output: The text provides a list of references related to shift work, circadian dysregulation, and their potential effects but does not directly address the negative impact of shift work on metabolic health. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: This demonstrates how tracking and monitoring change over time could allow insight into emerging disease. This is particularly useful for clinicians working in health care settings with limited time and resources and where cancer is a relatively rare occurrence among the burden of other diseases. In addition to the detection of signals, smartphones and wearables could alert the user to the need for health care consultation and provide an endorsement to seek care. This could overcome the commonly reported barrier to presentation (“concern about bothering the doctor”) that arises when there is uncertainty about the need for care [26-28]. to Successful ImplementationOverviewDespite the promise of smartphones and wearables for early detection of cancer, there are several hurdles to implementation that require attention. Key barriers to success are outlined here, alongside suggestions for how these may be addressed with future research. Signals and Accuracy and UtilityThe use and adoption of smartphones and wearables in this context require robust research into the selection of a signal, the development of sensors, and the generating evidence of accuracy and utility of those sensors in the real world. This includes identifying physiologic (or emerging pathophysiologic) signals that are most predictive of cancer, determining how often these need to be collected, and elucidating what other data would add precision to results (eg, age, risk factors, and presence of symptoms). For technology developers, sensors are usually designed and prioritized for a number of potential applications, mainly targeting overall health and wellness rather than diagnostic capabilities per se. Prioritizing these research and development efforts for cancer detection specifically over and above other priorities could be challenging to justify for business development reasons. Relatedly, the original intended commercial purpose of existing sensors may not have been connected to cancer detection. To make headway in this field of research, technology developers and device users will need to be willing to provide access to data for research. allows device users to share their data with third-party organizations under the right to portability. Developing systems to facilitate data sharing, in formats compatible with health data, could allow the generation of new data sets to signal cancer risk. This will prevent duplication of effort and maximize the use of existing data for public benefit. While initial evidence on the accuracy of a sensor to detect a given health signal could involve case-control studies (eg, individuals recently diagnosed with cancer and matched controls), subsequent research would likely require large prospective cohorts. [SEP] Justify:

Output: The text corroborates the claim by discussing how technologies such as smartphones and wearables can provide insights into emerging diseases and facilitate early detection. It highlights the importance of tracking signals and how these technologies may alert users to seek consultation, thereby enhancing the early detection capabilities for diseases like cancer. This aligns with the claim that AI in healthcare can assist in early disease detection.


Input: Task: contrast the claim using the given text. Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: 3). Generally, surface heating significantly influences PBlayer dynamics, playing a key role in shaping the PBlayer state in cities17,18. The changes in PBlayer depth by the adoption of RPVSPs are seen in the Kolkata urban region during the heatwave period. It is evident that the city-scale deployment of RPVSPs increases the PBlayer height over the urban region and the maximum increase (~415 m to ~615. 6 m) was simulated by the most aggressive scenario (that is, RPVSPs 100%). During the heatwave period, the resulting increases in the convective PBlayer height indicate that the lower atmosphere has a great potential to mix air pollutants vertically, with possible consequences for air quality. The diurnal variability of PBlayer depth was simulated with the effects of the implementation of RPVSP technology at the city scale. The height of PBlayer depth increases significantly by city-scale deployment of RPVSP technology. The maximum increase of PBlayer height at 14:00 LT is about 165. 3 m for RPVSPs 25%, 354. 4 m for RPVSPs 50%, 456. 4 m for RPVSPs 75% and 615. 6 m for RPVSPs 100%. In the late afternoon (18:00 LT), the average increase of PBlayer depth over urban regions is about 126. 4 m, 235. 5 m, 328. 6 m and 415. 6 m for RPVSPs 25%, RPVSPs 50%, RPVSPs 75% and RPVSPs 100%, respectively. In the early morning, the PBlayer depth increased significantly over the urban region and the values are 105. 4 m for RPVSPs 25%, 220. 3 m for RPVSPs 50%, 310. 1 m for RPVSPs 75% and 356. 4 m for RPVSPs 100%. City-scale deployment of RPVSPs absorb solar energy, increasing Qsensible and turbulence, which raises the urban surface temperature and deepens the PBlayer in urban areas. The impact of RPVSPs on boundary layer structure is demonstrated through the simulated vertical profile of temperature, Hrelative and horizontal Wspeed was investigated during peak heatwave events (Fig. 4). RPVSPs intensify urban warming and convective mixing, potentially aiding air pollution dispersion. [SEP] Justify:

Output: The text contrasts the claim by detailing how the deployment of Rooftop Vegetation Potential Scenarios (RPVSPs) affects the urban environment, indicating that city-scale deployment actually intensifies urban warming and increases the planetary boundary layer (PBlayer) depth. It mentions that instead of cooling, these implementations raise urban surface temperatures and enhance convective mixing, which implies detrimental effects on cooling cities during heat waves, rather than supporting the claim that rooftop gardens alone can cool entire cities.


Input: Task: pass the claim using the given text. Claim: Shift work can negatively impact metabolic health. [SEP] Text: Folkard, S. Do permanent night workers show circadian adjustment? A review based on the endogenous melatonin rhythmChronobiol Int. 2008; 25:215-224CrossrefScopus (259)PubMed14. Akerstedt, T. Shift work and disturbed sleep/wakefulness(Lond). 2003; 53:89-94CrossrefScopus (818)PubMed15. Baker, F. C. ∙ Driver, H. S. Circadian rhythms, sleep, and the menstrual cycleSleep Med. 2007; 8:613-622Full Text(PDF)Scopus (330)PubMed16. Hundrup, Y. A. ∙ Jacobsen, R. K. ∙ Andreasen, A. H. . . . Validation of a 5-year risk score of hip fracture in postmenopausal women. . 2010; 16:1-1017. Hundrup, Y. A. ∙ Hoidrup, S. ∙ Obel, E. B. . . . The validity of self-reported fractures among Danish female nurses: comparison with fractures registered in the. 2004; 32:136-143CrossrefScopus (67)PubMed18. Lokkegaard, E. L. ∙ Johnsen, S. P. ∙ Heitmann, B. L. . . . The validity of self-reported use of hormone replacement therapy among Danish nursesGynecol Scand. 2004; 83:476-481CrossrefPubMed19. Chen, C. ∙ Ma, X. ∙ Zhong, M. . . . Extremely low-frequency electromagnetic fields exposure and female breast cancer risk: a meta-analysis based on 24, 338 cases and 60, 628 controlsRes Treat. 2010; 123:569-576CrossrefScopus (40)PubMed20. Davis, S. ∙ Mirick, D. K. ∙ Stevens, R. G. Night shift work, light at night, and risk of breast cancerJ Inst. 2001; 93:1557-1562CrossrefScopus (772)PubMed21. Hansen, J. Increased breast cancer risk among women who work predominantly at nightEpidemiology. 2001; 12:74-77CrossrefScopus (420)PubMed22. Schernhammer, E. S. [SEP] Justify:

Output: The text does not provide any specific information related to the claim regarding the negative impacts of shift work on metabolic health. Instead, it appears to list various studies and authors without directly addressing the claim.


Input: Task: corroborate the claim using the given text. Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: The detection of cancer remains one of the most prominent priorities of many health systems, governments, and private and public research funders [63,64], and “leaving no stone unturned” in technologies that could potentially improve early detection is a priority. The rapid advances in the hardware (ie, sensors) and software embedded in smartphones and wearables offer exciting and potentially untapped opportunities to detect early warning signs of cancer that may otherwise be missed. The research and development needed to advance this field include the selection of appropriate signals and development of effective sensors followed by robust clinical research into accuracy in real-world settings. This relies on the up-front specification of the target groups and their needs. Target product profiles should be developed specifically for cancer detection technologies, and innovators should consult these and consider regulatory challenges early in the process of development, to design products in line with the requirements of individuals, clinicians, and health care systems. The potential negative consequences of this type of technology should be acknowledged and investigated up-front, and mitigations should be incorporated into the design and implementation strategies. To avoid exacerbation of inequalities in cancer outcomes, research into the use of wearables and smartphone technologies in cancer detection should be done with an equity lens to ensure that products are developed for those who have poorer health outcomes, for whom new innovations could have the most impact. There is a need for research to explore the patient, public, and health care perspectives about the use of smartphones and wearables for the early detection of cancer while this field is in its infancy, so that these can be incorporated into product design to optimize acceptability and adherence, avoid unintended consequences, and maximize the chance of their success. 2019. URL: [accessed 2024-04-25] Digital health innovation action plan. U. S. Food and Drug Administration. 2020. URL: [accessed 2024-04-25] Domingo-'Sullivan J, et al. Medical devices, smart drug delivery, wearables and technology for the treatment of diabetes mellitus. Deliv Rev. 2022;185:114280. [FREE Full text] [CrossRef] [Medline]Elliss- Routes to diagnosis for cancer—determining the patient journey using multiple routine data sets. Cancer. 2012;107(8):1220-1226. [FREE Full text] [CrossRef] [Medline]NHS Digital. 2023. [SEP] Justify:

Output: The text supports the claim by discussing how advancements in technologies, particularly through hardware and software in smartphones and wearables, create opportunities for early cancer detection. It emphasizes the importance of integrating effective sensors and clinical research into real-world settings, which aligns with the idea that AI and related technologies can enhance disease detection through innovative image analysis and monitoring.


Input: Task: contrast the claim using the given text. Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: During peak heatwave events, implementing 100% RPVSPs over building roofs impacts the atmosphere up to 1. 2 km. Hrelative decreases within the urban boundary layer, attributed to higher urban surface temperatures and reduced evaporation. RPVSPs enhance vertical Wspeed, reaching up to 1. 2 m s−1 during daytime, extending up to 0. 8 km in the urban boundary layer. The technology exacerbates urban temperature and lowers Hrelative, driven by changes in vertical mixing. Stronger vertical winds indicate enhanced energy transport from the surface to higher atmospheric layers. RPVSPs increase Qsensible, intensifying vertical mixing and deepening the PBlayer over the urban domain. In addition, the weaker vertical mixing over urban areas results in the advection of moist air coming from surrounding rural areas to urban areas and, consequently, the atmosphere beyond a certain height over the urban domain is barely affected by surface condition19. The weaker vertical mixing further developed stronger advection. However, this study does not indicate the stronger advection of moist air from rural areas because of the stronger vertical mixing of RPVSP scenarios. Surface heating of urban areas by RPVSP scenarios is the main important factor for controlling the vertical Wspeed in urban areas that enhances further development of a large convective roll over urban areas20. Fig. 4: Changes in simulated vertical meteorological variables. a–c, The changes show the vertical distribution profile in simulated standard surface meteorological fields air temperature (a), Hrelative (b) and wind fields (c) between building rooftops with RPVSP scenarios (100% coverage) and building rooftops without RPVSP scenarios along point A to point B (marked in Supplementary Fig. 8) during peak hour (14:00 LT) over Kolkata. Full size imageRegional impacts on sea breeze circulationsThe height of the PBlayer is closely associated with the advection of the sea breeze in tropical coastal cities. RPVSPs have the potential to modify sea breeze circulation by altering surface conditions and the boundary layer structure, potentially triggering localized circulation patterns in urbanized regions. The urban surface affects the Qlatent, Qsensible and momentum flux, while boundary layer height and surface convergence help to modify the sea breeze circulation impact on the urban atmosphere. [SEP] Justify:

Output: The text contrasts the claim by indicating that while RPVSPs (Rooftop Photovoltaic Systems) can influence atmospheric conditions and may enhance energy transport, it does not support the notion that rooftop gardens alone can cool entire cities. Instead, it emphasizes that the increased surface heating in urban areas, attributed to RPVSP scenarios, is a critical factor for controlling vertical wind speeds. This implies that merely having rooftop gardens is insufficient for cooling entire cities, especially during heat waves.


Input: Task: pass the claim using the given text. Claim: Olfactory receptors may operate through quantum effects. [SEP] Text: Peer review processEditorsReviewer #1Reviewer #2Reviewer #3Author ResponsePeer review processNot revised: Preprint includes the authors’ original preprint (without revision), an eLife assessment, public reviews, and a provisional response from the authors. Read more about eLife’s peer review process. of of Cambridge, Cambridge, United KingdomReviewer #1 (Public Review): Summary: Both flies and mammals have D1-like and D2-like dopamine receptors, yet the role of D2-like receptors in Drosophila learning and memory remains underexplored. The paper by Qi et al. investigates the role of the D2-like dopamine receptor D2R in single pairs of dopaminergic neurons (DANs) during single-odor aversive learning in the Drosophila larva. First, they use confocal imaging to screen driver strains with expression in only single pairs of dopaminergic neurons. Next, they use thermogenetic manipulations of one pair of DANs (DAN-c1) to implicate DAN-c1 activity during larval aversive learning. They then use confocal imaging to demonstrate expression of D2R in the DANs and mushroom body of the larval brain. Finally, they show that optogenetic activation during training phenocopies D2R knockdown in these neurons: aversive learning is impaired when DAN-c1 is targeted, while appetitive and aversive learning are impaired when the mushroom body is manipulated. Qi et al. thus propose a model in which D2R limits excessive dopamine release to facilitate successful olfactory learning. Strengths: The paper reproduces prior findings by Qi and Lee (2014), which demonstrated that D2R knockdown in DL1 DANs or the mushroom body impairs aversive olfactory learning in Drosophila larvae. The authors extended this previous work by screening 57 GAL4 drivers to identify tools that drive expression in individual DANs and used one of the tools, the R76F02-AD; R55C10-DBD driver, to manipulate DAN-c1 neurons with greater specificity. They also show that GFP-tagged D2R is expressed in most DANs and the mushroom body. [SEP] Justify:

Output: The text does not address olfactory receptors or any quantum effects related to them. Instead, it focuses on research related to dopamine receptors and their role in Drosophila learning and memory. Therefore, it is irrelevant to the claim about olfactory receptors and quantum effects.


Input: Task: corroborate the claim using the given text. Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: [FREE Full text] [CrossRef] [Medline]. Deep-learning-based smartphone application for self-diagnosis of scleral jaundice in patients with hepatobiliary and pancreatic diseases. J Pers Med. 2021;11(9):928. [FREE Full text] [CrossRef] [Medline]Sangers T, Reeder S, van der Validation of a market-approved artificial intelligence mobile health app for skin cancer screening: a prospective multicenter diagnostic accuracy study. Dermatology. 2022;238(4):649-656. [FREE Full text] [CrossRef] [Medline]Boennelykke A, Jensen H, Østgård Cancer risk in persons with new-onset anaemia: a population-based cohort study in Denmark. BMC Cancer. 2022;22(1):805. [FREE Full text] [CrossRef] [Medline]. The risk of pancreatic cancer in symptomatic patients in primary care: a large case-control study using electronic records. Cancer. 2012;106(12):1940-1944. [FREE Full text] [CrossRef] [Medline] Wearable sensor technology to predict core body temperature: a systematic review. Sensors (Basel). 2022;22(19):7639. [FREE Full text] [CrossRef] [Medline] Phases of metabolic and soft tissue changes in months preceding a diagnosis of pancreatic ductal adenocarcinoma. Gastroenterology. 2019;156(6):1742-1752. [FREE Full text] [CrossRef] [Medline]. Digital single-image smartphone assessment of total body fat and abdominal fat using machine learning. Sensors (Basel). 2022;22(21):8365. [FREE Full text] [CrossRef] [Medline] Non-invasive estimation of hemoglobin using a multi-model stacking regressor. . 2020;24(6):1717-1726. [CrossRef] [Medline]-. Association between purchase of over-the-counter medications and ovarian cancer diagnosis in the (CLOCS): observational case-control study. . 2023;9:e41762. [FREE Full text] [CrossRef] [Medline]. [SEP] Justify:

Output: The text corroborates the claim by mentioning various studies and applications of machine learning and deep learning in healthcare, specifically for tasks such as self-diagnosis of scleral jaundice and skin cancer screening. This indicates that AI technologies are being utilized to analyze images for early disease detection, aligning with the claim that AI can assist in early disease diagnosis.


Input: Task: contrast the claim using the given text. Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: Recently, the intensification of sea breeze circulation is strongly regulated by the city-scale synoptic background climate, which plays a critical role in modulating the prevailing wind near the surface21. The local circulation is generated by the thermal incongruity between land and sea22,23 and, on the synoptic scale, wind. This local circulation can extend more than 100 km horizontally inland24 and vertically may reach up to 3–4 km during the mid-afternoon. Due to the effect of urban signature, the advancement of sea breeze circulation can be accelerated for many hours25. RPVSPs alter these factors, increasing PBlayer height and Wspeed in urban areas. The adoption of RPVSPs accelerates sea breeze circulation, increasing vertical Wspeed by 3–5 m s−1 over urban regions. Due to drag forces, high-rise buildings and high surface roughness delay the arrival of sea breeze in urban cores (Supplementary Fig. 4). RPVSPs induce sensible heating and enhanced wind fields, affecting the urban climate during heatwaves. City-scale RPVSP adoption alters the thermal pressure gradient, raising Tambient by up to 1. 5 °C and horizontal Wspeed by 1. 2 m s−1. Therefore, increasing the coverage of the roof surface by RPVSPs induces sensible heating and increased wind fields, resulting in feedback within the urban local climate during peak hours in heatwave events. RPVSPs enhance the advective flow between the city and its environs, accelerating local warming in urban environments. They create a regional low, which can increase the horizontal and vertical Wspeed over the urbanized region. DiscussionWe conducted a comprehensive assessment of the study by comparing its findings with similar data from 30 existing case studies across various scales, including urban, continental and global contexts. The comparison is to assess the response of our model when using RPVSPs at the city scale, with results obtained from other cities with different background climate conditions. The case studies were categorized into two groups: (1) simulation-based studies and (2) observational studies. Within each group, the studies were further classified on the basis of their thermal effects into four categories: daytime warming, nighttime cooling, daytime cooling and nighttime warming (Fig. 5 and 2). These case studies utilized both observational and modeling approaches, providing critical insights for the RPVSP community. [SEP] Justify:

Output: The text contrasts the claim by explaining how the implementation of Roof-Planter Vegetated Surface Practices (RPVSPs) alters local climate dynamics, potentially leading to increased heating rather than cooling in urban areas. It indicates that RPVSPs induce sensible heating and enhance wind fields, thus affecting the urban climate during heatwaves rather than solely providing cooling. Additionally, it mentions that high-rise buildings and surface roughness delay the arrival of sea breezes in urban cores, which suggests that rooftop gardens may not effectively cool entire cities as the claim suggests.


Input: Task: pass the claim using the given text. Claim: Olfactory receptors may operate through quantum effects. [SEP] Text: Although the authors only train larvae with a single odor, they demonstrate that driving D2R knockdown in DAN-c1 neurons impairs aversive learning, as do other loss-of-function manipulations of DAN-c1 neurons. Weaknesses: The authors claim to have identified drivers that label single DANs in Figure 1, but their confocal images in Figure S1 suggest that many of those drivers label additional neurons in the larval brain. It is also not clear why only some of the 57 drivers are displayed in Figure S1. Critically, R76F02-AD; R55C10-DBD labels more than one neuron per hemisphere in Figure S1c, and the authors cite Xie et al. (2018) to note that this driver labels two DANs in adult brains. Therefore, the authors cannot argue that the experiments throughout their paper using this driver exclusively target DAN-c1. Missing from the screen of 57 drivers is the driver MB320C, which typically labels only PPL1-γ1pedc in the adult and should label DAN-c1 in the larva. If MB320C labels DAN-c1 exclusively in the larva, then the authors should repeat their key experiments with MB320C to provide more evidence for DAN-c1 involvement specifically. The authors claim that the SS02160 driver used by Eschbach et al. (2020) labels other neurons in addition to DAN-c1. Could the authors use confocal imaging to show how many other neurons SS02160 labels? Given that both Eschbach et al. and Weber et al. (2023) found no evidence that DAN-c1 plays a role in larval aversive learning, it would be informative to see how SS02160 expression compares with the driver the authors use to label DAN-c1. The claim that DAN-c1 is both necessary and sufficient in larval aversive learning should be reworded. Such a claim would logically exclude any other neuron or even the training stimuli from being involved in aversive learning (see Yoshihara and Yoshihara (2018) for a detailed discussion of the logic), which is presumably not what the authors intended because they describe the possible roles of other DANs during aversive learning in the discussion. [SEP] Justify:

Output: The text does not address the claim about olfactory receptors and quantum effects. Instead, it focuses on the shortcomings of a study related to aversive learning and neuronal labeling in larvae, making the content irrelevant to the assertion regarding olfactory receptors.


Input: Task: corroborate the claim using the given text. Claim: AI in healthcare can help detect diseases early with image analysis. [SEP] Text: Worrying about wasting GP time as a barrier to help-seeking: a community-based, qualitative study. Gen Pract. 2016;66(648):e474-e482. [FREE Full text] [CrossRef] [Medline] Public awareness of cancer in Britain: a population-based survey of adults. Cancer. 2009;101(Suppl 2):S18-S23. [FREE Full text] [CrossRef] [Medline]. Cancer symptom awareness and barriers to symptomatic presentation in England--are we clear on cancer? Cancer. 2015;113(3):533-542. [FREE Full text] [CrossRef] [Medline]van der Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review. Health. 2022;4(6):e466-e476. [FREE Full text] [CrossRef] [Medline]British association of dermatologists position statement on artificial intelligence (AI) interventions. of Dermatologists. 2022. URL: [accessed 2024-04-25] Schroé Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention. . 2020;17(1):127. [FREE Full text] [CrossRef] [Medline]Webb T, M. Monitoring interventions. In: editors. of Behavior Change. Cambridge. Press; 2020:537-553. Kroenke K. Studying symptoms: sampling and measurement issues. Med. 2001;134(9 Pt 2):844-853. [CrossRef] [Medline]. Regulatory considerations to keep pace with innovation in digital health products. NPJ Digit Med. 2022;5(1):121. [FREE Full text] [CrossRef] [Medline]Evidence standards framework for digital health technologies. for Health and Care Excellence. 2022. URL: [accessed 2024-04-25] Evaluating diagnostic strategies for early detection of cancer: the CanTest framework. BMC Cancer. 2019;19(1):586. [FREE Full text] [CrossRef] [Medline]Wang P, Kricka LJ. Current and emerging trends in point-of-care technology and strategies for clinical validation and implementation. [SEP] Justify:

Output: The text supports the claim by referencing a systematic review that discusses 'artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings.' This directly relates to the claim that AI can assist in the early detection of diseases through analysis of images.


Input: Task: contrast the claim using the given text. Claim: Rooftop gardens alone can cool entire cities during heat waves. [SEP] Text: The focus of these studies predominantly revolved around the assessment of first- and second-generation RPVSP systems. Among the 30 case studies, 17 reported daytime warming ranging from 0. 1 °C to 1. 5 °C. Only two studies found that RPVSPs might warm the atmosphere at night by 3. 1–4 °C. On the other hand, 13 studies indicated that RPVSPs could reduce Tambient throughout the day by 0. 02–1 °C, while 11 studies suggested that RPVSPs may decrease nighttime temperatures by 0. 12–2. 7 °C. Notably, five studies found that RPVSPs could contribute to both daytime warming by 0. 34–1. 5 °C and nighttime cooling by 0. 24–2. 7 °C. Our findings in the tropical region of Kolkata suggest that the widespread adoption of RPVSPs can significantly raise urban temperatures during the day, while typically cooling the urban environment at night. During summertime heatwaves, near-surface air temperatures in urban areas can increase by up to 1. 5 °C during the day. However, it has been observed that RPVSPs can reduce nighttime maximum near-surface air temperatures by up to 0. 6 °C when rooftops are fully covered with RPVSPs. Moreover, the extensive implementation of RPVSPs can lead to an increase in TSurface of urban grid cell of up to 3. 2 °C during peak hours, with an average cooling effect of up to 1. 4 °C during summer heatwaves at night. This increased temperature refers specifically due to the increased fraction of the RPVSPs on urban rooftops at the whole city scale. absorb approximately 90% of solar energy, converting up to ∼20% of it into electricity, while the remainder contributes to their warming. As a result, these RPVSPs often become very hot surfaces due to RPVSPs surface temperature reaching ∼70 °C during peak hours in the urban building rooftops (Fig. 6). Typically installed elevated above roof surfaces, RPVSPs have both a hot top surface and a hot underside. As air flows over these RPVSPs, it absorbs heat from both surfaces, amplifying the heating effect nearly twice as much as if the same temperature were present on a standard building surface. [SEP] Justify:

Output: The text contrasts the claim by providing data indicating that while rooftop systems (RPVSPs) might have some cooling effects at night, they also contribute to warming during the day. Specifically, the data shows that widespread adoption of RPVSPs can significantly raise urban temperatures during the day, especially during heatwaves, resulting in increases of up to 1.5 °C. Moreover, the studies indicated that RPVSPs can absorb considerable solar energy, leading to hotter surfaces that may not contribute effectively to cooling entire cities as the claim suggests.


Input: Task: pass the claim using the given text. Claim: Olfactory receptors may operate through quantum effects. [SEP] Text: Moreover, if DAN-c1 artificial activation conveyed an aversive teaching signal irrespective of the gustatory stimulus, then it should not impair aversive learning after quinine training (Figure 2k). While the authors interpret Figure 2k (and Figure 5) to indicate that artificial activation causes excessive DAN-c1 dopamine release, an alternative explanation is that artificial activation compromises aversive learning by overriding DAN-c1 activity that could be evoked by quinine. The authors should not necessarily expect that D2R enhancer driver strains would reflect D2R endogenous expression, since it is known that TH-GAL4 does not label p(PAM) dopaminergic neurons. Their observations of GFP-tagged D2R expression could be strengthened with an anti-D2R antibody such as that used by Lam et al. , (1999) or Love et al. , (2023). Finally, the authors could consider the possibility other DANs may also mediate aversive learning via D2R. Knockdown of D2R in DAN-g1 appears to cause a defect in aversive quinine learning compared with its genetic control (Figure S4e). It is unclear why the same genetic control has unexpectedly poor aversive quinine learning after training with propionic acid (Figure S5a). The authors could comment on why RNAi knockdown of D2R in DAN-g1 does not similarly impair aversive quinine learning (Figure S5b). #2 (Public Review): Summary: The study wanted to functionally identify individual DANs that mediate larval olfactory learning. Then search for DAN-specific driver strains that mark single dopaminergic neurons, which subsequently can be used to target genetic manipulations of those neurons. 56 GAL4 drivers identifying dopaminergic neurons were found (Table 1) and three of them drive the expression of GFP to a single dopaminergic neuron in the third-instar larval brain hemisphere. The DAN driver R76F02-AD;R55C10-DBD appears to drive the expression to a dopaminergic neuron innervating the lower peduncle (LP), which would be DAN-c1. Split-GFP reconstitution across synaptic partners (GRASP) technique was used to investigate the "direct" synaptic connections from DANs to the mushroom body. [SEP] Justify:

Output: The text does not address the claim regarding olfactory receptors and quantum effects. It discusses various aspects of aversive learning, dopamine release in relation to specific neuronal activity, and methodologies for identifying dopaminergic neurons. Therefore, it is irrelevant to the claim about the operation of olfactory receptors.