Influences of Indoor Air Temperatures on Empathy and Positive Affect.

The consequences of climate change are already visible, and yet, its effect on psychosocial factors, including the expression of empathy, affect, and social disconnection, is widely unknown. Outdoor conditions are expected to influence indoor conditions. Therefore, the aim of this study was to investigate the effect of indoor air temperature during work hours on empathy, positive and negative affect, and social disconnection. Participants (N = 31) were exposed, in a cross-over design, to two thermal conditions in a simulated office environment. Questions on empathy and social disconnection were administered before and after the exposure to each condition, while affect was measured throughout the day. Subjective thermal sensation and objective measures of mean skin temperature were considered. The results indicated a significant difference in empathy (F(1, 24) = 5.37, p = 0.03, with an η2 = 0.126) between conditions. Participants reported increases in empathy after exposure to the warm condition compared to the cool condition, in which reductions in empathy were reported. Although the same pattern was observed for positive affect, the difference was smaller and the results were not significant. Thermal sensation had a significant effect on changes in empathy too (F(1, 54) = 7.015, p = 0.01, with an R2 = 0.115), while mean skin temperature had no effect on empathy (F(1, 6) = 0.53, p = 0.89, with an R2 = 0.81). No effects were observed for positive and negative affect and social disconnection. Longitudinal studies are needed to support these findings.

Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling study.

BACKGROUND: Deep learning is a promising way to improve health care. Image-processing medical disciplines, such as pathology, are expected to be transformed by deep learning. The first clinically applicable deep-learning diagnostic support tools are already available in cancer pathology, and their number is increasing. However, data on the environmental sustainability of these tools are scarce. We aimed to conduct an environmental-sustainability analysis of a theoretical implementation of deep learning in patient-care pathology. METHODS: For this modelling study, we first assembled and calculated relevant data and parameters of a digital-pathology workflow. Data were breast and prostate specimens from the university clinic at the Institute of Pathology of the Rheinisch-Westfälische Technische Hochschule Aachen (Aachen, Germany), for which commercially available deep learning was already available. Only specimens collected between Jan 1 and Dec 31, 2019 were used, to omit potential biases due to the COVID-19 pandemic. Our final selection was based on 2 representative weeks outside holidays, covering different types of specimens. To calculate carbon dioxide (CO2) or CO2 equivalent (CO2 eq) emissions of deep learning in pathology, we gathered relevant data for exact numbers and sizes of whole-slide images (WSIs), which were generated by scanning histopathology samples of prostate and breast specimens. We also evaluated different data input scenarios (including all slide tiles, only tiles containing tissue, or only tiles containing regions of interest). To convert estimated energy consumption from kWh to CO2 eq, we used the internet protocol address of the computational server and the Electricity Maps database to obtain information on the sources of the local electricity grid (ie, renewable vs non-renewable), and estimated the number of trees and proportion of the local and world's forests needed to sequester the CO2 eq emissions. We calculated the computational requirements and CO2 eq emissions of 30 deep-learning models that varied in task and size. The first scenario represented the use of one commercially available deep-learning model for one task in one case (1-task), the second scenario considered two deep-learning models for two tasks per case (2-task), the third scenario represented a future, potentially automated workflow that could handle 7 tasks per case (7-task), and the fourth scenario represented the use of a single potential, large, computer-vision model that could conduct multiple tasks (multitask). We also compared the performance (ie, accuracy) and CO2 eq emissions of different deep-learning models for the classification of renal cell carcinoma on WSIs, also from Rheinisch-Westfälische Technische Hochschule Aachen. We also tested other approaches to reducing CO2 eq emissions, including model pruning and an alternative method for histopathology analysis (pathomics). FINDINGS: The pathology database contained 35 552 specimens (237 179 slides), 6420 of which were prostate specimens (10 115 slides) and 11 801 of which were breast specimens (19 763 slides). We selected and subsequently digitised 140 slides from eight breast-cancer cases and 223 slides from five prostate-cancer cases. Applying large deep-learning models on all WSI tiles of prostate and breast pathology cases would result in yearly CO2 eq emissions of 7·65 metric tons (t; 95% CI 7·62-7·68) with the use of a single deep-learning model per case; yearly CO2 eq emissions were up to 100·56 t (100·21-100·99) with the use of seven deep-learning models per case. CO2 eq emissions for different deep-learning model scenarios, data inputs, and deep-learning model sizes for all slides varied from 3·61 t (3·59-3·63) to 2795·30 t (1177·51-6482·13. For the estimated number of overall pathology cases worldwide, the yearly CO2 eq emissions varied, reaching up to 16 megatons (Mt) of CO2 eq, requiring up to 86 590 km2 (0·22%) of world forest to sequester the CO2 eq emissions. Use of the 7-task scenario and small deep-learning models on slides containing tissue only could substantially reduce CO2 eq emissions worldwide by up to 141 times (0·1 Mt, 95% CI 0·1-0·1). Considering the local environment in Aachen, Germany, the maximum CO2 eq emission from the use of deep learning in digital pathology only would require 32·8% (95% CI 13·8-76·6) of the local forest to sequester the CO2 eq emissions. A single pathomics run on a tissue could provide information that was comparable to or even better than the output of multitask deep-learning models, but with 147 times reduced CO2 eq emissions. INTERPRETATION: Our findings suggest that widespread use of deep learning in pathology might have considerable global-warming potential. The medical community, policy decision makers, and the public should be aware of this potential and encourage the use of CO2 eq emissions reduction strategies where possible. FUNDING: German Research Foundation, European Research Council, German Federal Ministry of Education and Research, Health, Economic Affairs and Climate Action, and the Innovation Fund of the Federal Joint Committee.

The Spread of Exhaled Air and Aerosols during Physical Exercise.

Physical exercise demonstrates a special case of aerosol emission due to its associated elevated breathing rate. This can lead to a faster spread of airborne viruses and respiratory diseases. Therefore, this study investigates cross-infection risk during training. Twelve human subjects exercised on a cycle ergometer under three mask scenarios: no mask, surgical mask, and FFP2 mask. The emitted aerosols were measured in a grey room with a measurement setup equipped with an optical particle sensor. The spread of expired air was qualitatively and quantitatively assessed using schlieren imaging. Moreover, user satisfaction surveys were conducted to evaluate the comfort of wearing face masks during training. The results indicated that both surgical and FFP2 masks significantly reduced particles emission with a reduction efficiency of 87.1% and 91.3% of all particle sizes, respectively. However, compared to surgical masks, FFP2 masks provided a nearly tenfold greater reduction of the particle size range with long residence time in the air (0.3-0.5 µm). Furthermore, the investigated masks reduced exhalation spreading distances to less than 0.15 m and 0.1 m in the case of the surgical mask and FFP2 mask, respectively. User satisfaction solely differed with respect to perceived dyspnea between no mask and FFP2 mask conditions.

Combining adaptive and heat balance models for thermal sensation prediction: A new approach towards a theory and data-driven adaptive thermal heat balance model.

The adaptive thermal heat balance (ATHB) framework introduced a method to account for the three adaptive principals, namely physiological, behavioral, and psychological adaptation, individually within existing heat balance models. This work presents a more detailed theoretical framework together with a theory-driven empirical determination toward a new formulation of the ATHBPMV . The empirical development followed a rigor statistical process known from machine learning approaches including training, validation, and test phase and makes use of a subset (N = 57 084 records) of the ASHRAE Global Thermal Comfort Database. Results show an increased predictive performance among a wide range of outdoor climates, building types, and cooling strategies of the buildings. Furthermore, individual findings question the common believe that psychological adaptation is highest in naturally ventilated buildings. The framework offers further opportunities to include a variety of context-related variables as well as personal characteristics into thermal prediction models, while keeping mathematical equations limited and enabling further advancements related to the understanding of influences on thermal perception.

Does thermal control improve visual satisfaction? Interactions between occupants' self-perceived control, visual, thermal, and overall satisfaction.

Occupants' satisfaction had been researched independently related to thermal and visual stimuli for many decades showing among others the influence of self-perceived control. Few studies revealed interactions between thermal and visual stimuli affecting occupant satisfaction. In addition, studies including interactions between thermal and visual stimuli are lacking different control scenarios. This study focused on the effects of thermal and visual factors, their interaction, seasonal influences, and the degree of self-perceived control on overall, thermal, and visual satisfaction. A repeated-measures laboratory study with 61 participants running over two years and a total of 986 participant sessions was conducted. Mixed model analyses with overall satisfaction as outcome variable revealed that thermal satisfaction and visual satisfaction are the most important predictors for overall satisfaction with the indoor environment. Self-perceived thermal control served as moderator between thermal satisfaction and overall satisfaction. Season had slight influence on overall satisfaction. Random effects explained the highest amount of variance, indicating that intra- and interindividual differences in the ratings of satisfaction are more prevalent than study condition. Future building design and operation plans aiming at a high level of occupant satisfaction should consider personal control opportunities and take into account the moderating effect of control opportunities in multimodal interactions.

Perception of repeated pain relief with controllable and uncontrollable pain.

BACKGROUND: The ultimate goal of pain research is to provide effective routes for pain relief. Nevertheless, the perception pain relief as a change in pain intensity and un-/pleasantness has only been rarely investigated. It has been demonstrated that pain relief has rewarding and reinforcing properties, but it remains unknown whether the perception of pain relief changes when pain reductions occur repeatedly. Further, it remains an open question whether the perception of pain relief depends on the controllability of the preceding pain. METHODS: In this study, healthy volunteers (N = 38) received five cycles of painful heat stimulation and reduction of this stimulation to a non-painful warm stimulation once in a condition with control of the stimulation and once without control. Participants rated perceived intensity and un-/pleasantness on visual analogue scales during the heat stimulation and immediately after its reduction. RESULTS: Results showed that perceived pain relief, estimated by the difference in ratings during ongoing heat stimulation and after its reduction, increased with repetitions. However, this increase levelled off after two to four repetitions. Further, perceived pain relief was larger in the condition without control compared to the condition with control. CONCLUSION: The perception of pain relief can be modulated similar to the perception of pain by stimulus characteristics and psychological factors. Mechanistic knowledge about such modulating factors is important, because they can determine, e.g., the amount of requested pain killers in clinical settings and the efficacy of pain relief as a reinforcing stimulus. SIGNIFICANCE: When in pain, pain relief can become an all-dominate goal. The perception of such pain relief can vary depending on external and internal characteristics and thus modulate, e.g., requests for pain killers in clinical settings. Here, we show that perceived intensity and pleasantness of pain relief changes with repetitions and whether the preceding pain is perceived as uncontrollable. Such mechanistic knowledge needs to be considered to maximize the effects of pain relief as a rewarding and reinforcing stimulus.

Publisher Correction: The Scales Project, a cross-national dataset on the interpretation of thermal perception scales.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

What does "moderate pain" mean? Subgroups holding different conceptions of rating scales evaluate experimental pain differently.

BACKGROUND: Pain ratings are almost ubiquitous in pain assessment, but their variability is high. Low correlations of continuous/numerical rating scales with categorical scales suggest that individuals associate different sensations with the same number on a scale, jeopardizing the interpretation of statistical results. We analysed individual conceptions of rating scales and whether these conceptions can be utilized in the analysis of ratings of experimental stimuli in pain-free healthy individuals and people with reoccurring/persistent pain. METHODS: Using a free positioning task, healthy participants (N = 57) and people with reoccurring/persistent pain (N = 57) ad libitum positioned pain descriptors on lines representing intensity and un-/pleasantness scales. Furthermore, participants rated experimental thermal stimuli on visual analogue scales with the same end anchors. A latent class regression approach was used to detect subgroups with different response patterns in the free positioning task, indicating different conceptions of pain labels, and tested whether these subgroups differed in their ratings of experimental stimuli. RESULTS: Subgroups representing different conceptions of pain labels could be described for the intensity and the un-/pleasantness scale with in part opposing response patterns in the free positioning task. Response patterns did not differ between people with and without pain, but in people with pain subgroups showed differential ratings of high intensity experimental stimuli. CONCLUSIONS: Individuals' conceptions of pain labels differ. These conceptions can be quantified and utilized to improve the analysis of ratings of experimental stimuli. Identifying subgroups with different conceptions of pain descriptions could be used to improve predictions of responses to pain in clinical contexts. SIGNIFICANCE: The present results provide a novel approach to incorporate individual conceptualizations of pain descriptors, which can induce large distortions in the analysis of pain ratings, in pain assessment. The approach can be used to achieve better pain estimates, representing individual conceptions of pain and achieving a better comparability between individuals but also between pain-free persons and patients with chronic pain. Particularly, in clinical settings this could improve quantification of perceived pain and the patient-clinician communication.

The Scales Project, a cross-national dataset on the interpretation of thermal perception scales.

Thermal discomfort is one of the main triggers for occupants' interactions with components of the built environment such as adjustments of thermostats and/or opening windows and strongly related to the energy use in buildings. Understanding causes for thermal (dis-)comfort is crucial for design and operation of any type of building. The assessment of human thermal perception through rating scales, for example in post-occupancy studies, has been applied for several decades; however, long-existing assumptions related to these rating scales had been questioned by several researchers. The aim of this study was to gain deeper knowledge on contextual influences on the interpretation of thermal perception scales and their verbal anchors by survey participants. A questionnaire was designed and consequently applied in 21 language versions. These surveys were conducted in 57 cities in 30 countries resulting in a dataset containing responses from 8225 participants. The database offers potential for further analysis in the areas of building design and operation, psycho-physical relationships between human perception and the built environment, and linguistic analyses.

Long-term monitoring data from a naturally ventilated office building.

Data was collected in the field, from an office building located in Frankfurt, Germany, over the period of 4 years. The building was designed as a low-energy building and featured natural ventilation for individual control of air quality as well as buoyancy-driven night ventilation in combination with a central atrium as a passive cooling strategy. The monitored data include in total 116 data points related to outdoor and indoor environmental data, energy related data, and data related to occupancy and occupant behaviour. Data points representing a state were logged with the real timestamp of the event taking place, all other data points were recorded in 10 minute intervals. Data were collected in 17 cell offices with a size of ~20 m2, facing either east or west). Each office has one fixed and two operable windows, internal top light windows between office and corridor (to allow for night ventilation into the atrium) and sun protection elements (operated both manually and automatically). Each office is occupied by one or two persons.

Drivers of diversity in human thermal perception - A review for holistic comfort models.

Understanding the drivers leading to individual differences in human thermal perception has become increasingly important, amongst other things due to challenges such as climate change and an ageing society. This review summarizes existing knowledge related to physiological, psychological, and context-related drivers of diversity in thermal perception. Furthermore, the current state of knowledge is discussed in terms of its applicability in thermal comfort models, by combining modelling approaches of the thermoneutral zone (TNZ) and adaptive thermal heat balance model (ATHB). In conclusion, the results of this review show the clear contribution of some physiological and psychological factors, such as body composition, metabolic rate, adaptation to certain thermal environments and perceived control, to differences in thermal perception. However, the role of other potential diversity-causing parameters, such as age and sex, remain uncertain. Further research is suggested, especially regarding the interaction of different diversity-driving factors with each other, both physiological and psychological, to help establishing a holistic picture.