Emission and capture characteristics of Chinese cooking-related fine particles.

Cooking can emit high concentrations of particles and gaseous pollutants. Cooking has contributed to the major source of indoor air pollutants, especially for particle pollutants in residential buildings. Many studies already analyzed the emission characteristics of Chinese cooking-related UFPs and PM2.5, while less for the fine particle size distributions. Currently, the fine particle emission characteristics of Chinese cooking need to be further investigated, since the mass size distribution of Chinese cooking is dominated by fine particles. This study determined the emission characteristics of PM1 and fine particles from three Chinese cooking methods. The capture efficiencies of particles were also measured by a modified indirect approach, including the impact of particle decay. The results showed that stir-fried vegetable and pan-fried meat dishes generated more fine particles at 0.542-1.5 µm. Besides, pan-fried and deep-fried meat dishes produce a higher generation of PM1. The fine particles (0.542-10 µm) number-based and volume-based size distributions of six dishes both presented a monodisperse behavior. The cooking methods are not a sensitive factor to the volume frequency of fine particle ranging from 0.542 to 10 µm. The averaged volume median and mode diameter for six typical Chinese dishes are 2.5 µm and 3.3 µm, respectively. The Sauter and DeBroukere mean diameter is 4.7 µm and 5.6 µm, respectively. The decay of fine particles increases with the particle diameter. The impact of particle decay on capture efficiency for 2-3 µm particles is about 5%. The capture efficiencies of pan-fried and deep-fried meat dishes are lower than that of vegetable dishes. In contrast, the capture efficiency for stir-fried meat dishes is higher than that of vegetable dishes. The capture efficiency for PM1 and 0.542-5 µm particles from six typical Chinese dishes were 60-90% on the IEC recommended exhaust flowrate.

Achieving better indoor air quality with IoT systems for future buildings: Opportunities and challenges.

With the development of IoT technology and low-cost indoor air quality (IAQ) sensors, the IoT-based IAQ monitoring platform has garnered significant research interest and demonstrated its potential in enhancing IAQ management. This study presents a comprehensive review of previous research on the development and application of IoT-based IAQ platforms in different built environments. It offers detailed insights into the design and implementation of recent IoT-based IAQ platforms. The findings indicate that the IoT-based IAQ platforms are able to provide reliable information for IAQ monitoring. To ensure quality control of the IoT-based IAQ platform, it is suggested to replace the sensors every 4-6 months for reliable monitoring. In another aspect, integrating data-driven technology into the platform is crucial for IAQ prediction and efficient control of ventilation systems, leveraging the wealth of data available from the IoT platform. According to recent studies that applied data-driven algorithms for IAQ management, it can be confirmed that the data-driven algorithms are able to prompt IAQ by providing either more information or a control strategy. However, it should be noted that only 9.1 % of the developed platforms integrated data-driven models for IAQ management. Based on our findings, current challenges and further opportunities are discussed. Future studies should focus on integrating data-driven algorithms into IoT-based IAQ platforms and developing digital twins that can be used for real building IAQ management. However, there is obvious tension between controlling ventilation for energy efficiency versus better air quality. It is important to make a balance between energy efficiency and better air quality according to the current situations of specific built environments. Also, the next generation of IoT-based IAQ platforms should include occupants in the loop to create a more occupant-centric IAQ management approach.

Comparing the effects of sun and wind on outdoor thermal comfort: A case study based on longitudinal subject tests in cold climate region.

Sun and wind are important physical factors that influence outdoor thermal comfort. This study compared the impact of sun and wind on outdoor thermal sensation by analyzing 3546 samples of subject test data during a case study in a cold climate city Tianjin, China. The data was collected from subject tests conducted under air temperatures ranging from 3.8 °C to 35.2 °C (mean 20.2 °C), wind from 0 to 4.8 m/s (mean 0.6 m/s), mean radiant temperature 1.8 to 68.9 °C (mean 36.8 °C) in Tianjin, China. In this particular study, the sun was found to be a more significant factor than wind during the test. Standardized linear regression of the pooled dataset revealed that the contributions of air temperature, sun, wind, and humidity to thermal sensation were 56%, 29.4%, 8.8%, and 5.9%, respectively. When compared under different air temperature ranges, the effect of sun was more than two times greater that of wind. When the air temperature was in the range of 5-10 °C, solar exposure increased the thermal sensation by more than 2 units, but a reduction in wind speed had no observable effect on thermal sensation. When the air temperature was as high as 30-35 °C, increasing the wind by up to 2 m/s lowered the thermal sensation in the shade, but not in the sun. A summary of pedestrian level wind measured in real urban spaces in 28 previous studies indicated that urban spaces generally have low wind speeds, with the median value of mean wind speed of 0.8 m/s. The results of this study provide useful information for designs to creating comfortable urban open spaces.

Estimating long-term time-resolved indoor PM2.5 of outdoor and indoor origin using easily obtainable inputs.

To evaluate the separate impacts on human health and establish effective control strategies, it is crucial to estimate the contribution of outdoor infiltration and indoor emission to indoor PM2.5 in buildings. This study used an algorithm to automatically estimate the long-term time-resolved indoor PM2.5 of outdoor and indoor origin in real apartments with natural ventilation. The inputs for the algorithm were only the time-resolved indoor/outdoor PM2.5 concentrations and occupants' window actions, which were easily obtained from the low-cost sensors. This study first applied the algorithm in an apartment in Tianjin, China. The indoor/outdoor contribution to the gross indoor exposure and time-resolved infiltration factor were automatically estimated using the algorithm. The influence of outdoor PM2.5 data source and algorithm parameters on the estimated results was analyzed. The algorithm was then applied in four other apartments located in Chongqing, Shenyang, Xi'an, and Urumqi to further demonstrate its feasibility. The results provided indirect evidence, such as the plausible explanations for seasonal and spatial variation, to partially support the success of the algorithm used in real apartments. Through the analysis, this study also identified several further development directions to facilitate the practical applications of the algorithm, such as robust long-term outdoor PM2.5  monitoring using low-cost light-scattering sensors.

Associations of indoor carbon dioxide concentrations, air temperature, and humidity with perceived air quality and sick building syndrome symptoms in Chinese homes.

The indoor environment influences occupants' health. From March 1, 2018, to February 28, 2019, we continuously monitored indoor temperature (T), relative humidity (RH), and CO2 concentration in bedrooms via an online system in 165 residences that covered all five climate zones of China. Meanwhile, we asked one specific occupant in each home to complete questionnaires about perceived air quality and sick building syndrome (SBS) symptoms at the end of each month. Higher CO2 concentration was significantly associated with a higher percentage of perceived stuffy odor and skin SBS symptoms. Higher relative humidity was associated with higher percentage of perceived moldy odor and humid air, while lower RH was associated with a higher percentage of perceived dry air. Occupants who lived in residences with high RH were less likely to have mucosal and skin SBS symptoms (adjusted odds ratio (AOR): 0.73-0.78). However, the benefit of high humidity for perceived dry air and skin dryness symptoms is weaker if there is a high CO2 concentration level.

A recurrent neural network using historical data to predict time series indoor PM2.5 concentrations for residential buildings.

Due to the severe outdoor PM2.5 pollution in China, many people have installed air-cleaning systems in homes. To make the systems run automatically and intelligently, we developed a recurrent neural network (RNN) that uses historical data to predict the future indoor PM2.5 concentration. The RNN architecture includes an autoencoder and a recurrent part. We used data measured in an apartment over the course of an entire year to train and test the RNN. The data include indoor/outdoor PM2.5 concentration, environmental parameters and time of day. By comparing three different input strategies, we found that a strategy employing historical PM2.5 and time of day as inputs performed best. With this strategy, the model can be applied to predict the relatively stable trend of indoor PM2.5 concentration in advance. When the input length is 2 h and the prediction horizon is 30 min, the median prediction error is 8.3 µg/m3 for the whole test set. For times with indoor PM2.5 concentrations between (20,50] µg/m3 and (50,100] µg/m3 , the median prediction error is 8.3 and 9.2 µg/m3 , respectively. The low prediction error between the ground-truth and predicted values shows that the RNN can predict indoor PM2.5 concentrations with satisfactory performance.

Deep learning for automated cerebral aneurysm detection on computed tomography images.

PURPOSE: Cerebrovascular aneurysms are being observed with rapidly increasing incidence. Therefore, tools are needed for accurate and efficient detection of aneurysms. We used deep learning techniques with CT angiography acquired from multiple medical centers and different machines to develop and evaluate an automatic detection model. METHODS: In this study, we have introduced a deep learning model, the faster RCNN model, in order to develop a tool for automatic detection of aneurysms from medical images. The inputs of the model were 2D nearby projection (NP) images from 3D CTA, which were made by the NP method proposed in this study. This method made aneurysms clearly visible on images and improved the model's performance. The study included 311 patients with 352 aneurysms, selected from three hospitals, and 208 and 103 of these patients, respectively, were randomly selected to train and test the models. RESULTS: The sensitivity of the trained model was 91.8%. For aneurysm sizes larger than 3 mm, the sensitivity of successful aneurysm detection was 96.7%. We achieved state-of-the-art sensitivity for > 3 mm aneurysms. The sensitivities also indicated that there was no significant difference among aneurysms at different locations in the body. Computing time for the detection process was less than 25 s per case. CONCLUSIONS: We successfully developed a deep learning model that can automatically detect aneurysms. The model performed well for aneurysms of different sizes or in different locations. This finding indicates that the deep learning model has the potential to vastly improve clinician performance by providing automated aneurysm detection.

An eight-city study of volatile organic compounds in Chinese residences: Compounds, concentrations, and characteristics.

This study conducted on-site measurements of indoor volatile organic compounds (VOCs) in 251 occupied residences in China, with multiple visits throughout a whole year. Over 1000 samples were collected for measurement of VOCs in 8 cities, covering different climate regions. Overall, the concentrations of total VOCs (TVOCs) in occupied residences are in the range of 104-1151 µg/m3, with 20% of the samples over the Chinese standard of 600 µg/m3. A higher concentration was evident in the summer (mean = 705 µg/m3) compared to other seasons, especially winter (mean = 289 µg/m3). The TVOCs of residences in areas with central heating (severe cold regions and cold regions) are generally higher than those in areas without central heating. In winter, temperature was the dominant factor, whereas in summer, the building infiltration rate was the key factor influencing the indoor TVOC levels. The TVOCs concentration was also found to be directly proportional to the city economy level. Twenty-nine VOC species with a detection frequency higher than 40% were identified in all samples. Toluene is the most common VOC, with the highest detection rate (90%). The median concentration for a single VOC was between 1 and 14 µg/m3. Aldehydes were found to be the largest contributors to total VOCs in the Chinese residential buildings (mass proportion 22%), followed by benzene series (20%), alkenes (18%), and alkanes (15%). Aldehydes, especially long-chain saturated carbonyls, are likely to be the characteristic VOCs in the Chinese dwellings, with Chinese cooking as the major emission source. In addition, n-butane/i-butane showed maximum concentration in some residences (approximately 105 µg/m3 higher than other VOCs) owing to cooking fuel.

Air change rates in urban Chinese bedrooms.

The ventilation modalities in most Chinese residences are infiltration and opening windows. We measured infiltration rates and air change rates at night, with no attempt to change occupants' behaviors, of urban residences in five climate zones of China during four seasons. Using the CO2 decay method, we found the median infiltration rate for 294 residences to be 0.34 h-1 . Using occupant-generated CO2 as tracer gas, we determined air change rates over the course of 1 year in 46 bedrooms at night from mass balance considerations. In 54% of the measurements, windows were closed, so ventilation was only by infiltration. Windows were mainly closed when the outdoor temperature was below 15°C and above 26°C. The median infiltration rates did not differ appreciably among seasons and climate zones and were always less than 0.45 h-1 .

Detection, enumeration, and characterization of immune cells infiltrating melanoma tumors.

Tumor-infiltrating immune cells have long been thought to affect tumor growth. In recent years, large retrospective studies have shown that the nature and polarization of the immune cells found within the tumor microenvironment impact not only the growth of the primary tumor, but also disease progression and patient survival. This has triggered considerable interest for an in depth analysis of the tumoral immune microenvironment and has created a need for standardized methods to characterize tumor-infiltrating immune cells. Here, we describe three approaches that can be used in mouse and human melanoma tumors.

Immune predictors of cancer progression.

The immune system has multiple, complex, and sometimes opposing roles during cancer progression. While immune-compromised individuals have a higher incidence of cancers, inflammation is also associated with increased risk of disease progression. It is becoming apparent that simple measures of immune responses in the blood are of limited use in cancer. Instead, the importance of the exact identity and functional characteristics of tumor-infiltrating immune cells is increasingly recognized. This realization has led to recent studies that have revealed a critical role for chemokine expression in the tumor microenvironment and suggested a therapeutic potential of manipulating intratumoral expression of chemokines to alter the local immune milieu.

Lung CD103+ dendritic cells efficiently transport influenza virus to the lymph node and load viral antigen onto MHC class I for presentation to CD8 T cells.

The uptake, transport, and presentation of Ags by lung dendritic cells (DCs) are central to the initiation of CD8 T cell responses against respiratory viruses. Although several studies have demonstrated a critical role of CD11b(low/neg)CD103(+) DCs for the initiation of cytotoxic T cell responses against the influenza virus, the underlying mechanisms for its potent ability to prime CD8 T cells remain poorly understood. Using a novel approach of fluorescent lipophilic dye-labeled influenza virus, we demonstrate that CD11b(low/neg)CD103(+) DCs are the dominant lung DC population transporting influenza virus to the posterior mediastinal lymph node as early as 20 h postinfection. By contrast, CD11b(high)CD103(neg) DCs, although more efficient for taking up the virus within the lung, migrate poorly to the lymph node and remain in the lung to produce proinflammatory cytokines instead. CD11b(low/neg)CD103(+) DCs efficiently load viral peptide onto MHC class I complexes and therefore uniquely possess the capacity to potently induce proliferation of naive CD8 T cells. In addition, the peptide transporters TAP1 and TAP2 are constitutively expressed at higher levels in CD11b(low/neg)CD103(+) DCs, providing, to our knowledge, the first evidence of a distinct regulation of the Ag-processing pathway in these cells. Collectively, these results show that CD11b(low/neg)CD103(+) DCs are functionally specialized for the transport of Ag from the lung to the lymph node and also for efficient processing and presentation of viral Ags to CD8 T cells.

Differential signal transduction, membrane trafficking, and immune effector functions mediated by FcgammaRI versus FcgammaRIIa.

Receptors for the fragment crystallizable region of immunoglobulin-G (FcgammaRs) play an important role in linking the humoral and cellular arms of the immune response. In this study, we present a comprehensive functional comparison of 2 human Fc-receptors, FcgammaRI and FcgammaRIIa. Activation of FcgammaRI results in a novel signaling cascade that links phospholipase D1 to sphingosine kinase-1 in U937 cells and primary human monocytes. This induces the expression of proinflammatory mediators and is associated with trafficking of immune complexes into human leukocyte antigen-DM positive antigen-processing compartments coupled with improved MHC class II-mediated antigen presentation to T lymphocytes. In contrast, activation of FcgammaRIIa elicits signaling through phospholipase Cgamma1, resulting in increases in intracellular calcium, activation of nicotinamide adenine dinucleotide phosphate-oxidative burst, and differential membrane trafficking combined with impaired antigen presentation and proinflammatory cytokine expression. These data provide a mechanistic insight into the disparate activities associated with Fc receptors in immunity, namely, reinforcement of immune responses through stimulation of proinflammatory signaling and antigen presentation, versus the maintenance of immunologic homeostasis through the noninflammatory clearance of immune complexes.

Fc gamma receptor biology and systemic lupus erythematosus.

The maintenance of immune homeostasis and the regulation of pro-inflammatory responses that underlie autoimmune pathology require a coordinated interplay between cytokines, cellular receptors and downstream signaling pathways. The family of fragment crystallizable receptors for immunoglobulin G (IgG) (Fc gammaR) represents a good example of how controlling the simultaneous triggering of activatory and/or inhibitory receptors results in a balanced immune response. Fc gammaRs play a crucial role in linking the antibody-mediated recognition of pathogens, allergens or auto-antigens to the cellular arm of the immune response. In this review, we detail how dysfunction in Fc gammaR pathways is linked to the development of the autoimmune disease, systemic lupus erythematosus.