Search

Due to the influences of buildings, geographical and geomorphological environments, road conditions, etc., the probabilities and numbers of casualties in different areas after an earthquake are different. Accordingly, we propose the concept of the lethal level, which attains different grades representing the mortality rate of differing intensities. Different regions have unique lethal levels, and regional lethal levels are affected mainly by the proportion of each building type and the corresponding lethal level, as different types of buildings also have unique lethal levels. Based on data of 52 historical earthquake disasters, we constructed a lethal level calculation model and obtained the lethal level of each building type. The results reveal that the lethal level ranges of different building types are fixed and unequal; moreover, the ranges of different building types overlap each other. The lethal level range of adobe structures is 0.85–1, that of civil structures is 0.75–0.95, that of brick-wood structures is 0.6–0.9, that of brick-concrete structures is 0.33–0.6, that of wood structures is 0.2–0.35, and that of reinforced concrete structures is 0.1–0.25. Based on the lethal levels of these building types, the overall level of a region can be quantified and graded, and this classification does not depend on the geographical location or administrative boundaries. In pre-earthquake evaluation efforts, the lethal level of an area can be derived through field research. After an earthquake, the number of casualties can be quickly assessed based on the mortality rate corresponding to the intensity of the area. This approach can further provide scientific support for risk zoning and risk assessment research.

Surface longwave radiation (SLR) plays a pivotal role in the Earth's energy balance, influencing a range of environmental processes and climate dynamics. As the demand for high spatial resolution remote sensing products grows, there is an increasing need for accurate SLR retrieval with enhanced spatial detail. This study focuses on the development and validation of models to estimate SLR using measurements from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Given the limitations posed by fewer spectral bands and data products in ASTER compared to moderate-resolution sensors, the proposed approach combines an atmospheric radiative transfer model MODerate resolution atmospheric TRANsmission (MODTRAN) with the Light Gradient Boosting Machine algorithm to estimate SLR. The MODTRAN simulations were performed to construct a representative training dataset based on comprehensive global atmospheric profiles and surface emissivity spectra data. Global sensitivity analyses reveal that key inputs influencing the accuracy of SLR retrievals should reflect surface thermal radiative signals and near-surface atmospheric conditions. Validated against ground-based measurements, surface upward longwave radiation (SULR) and surface downward longwave radiation (SDLR) using ASTER thermal infrared bands and surface elevation estimations resulted in root mean square errors of 17.76 W/m2 and 25.36 W/m2, with biases of 3.42 W/m2 and 3.92 W/m2, respectively. Retrievals show systematic biases related to extreme temperature and moisture conditions, e.g., causing overestimation of SULR in hot humid conditions and underestimation of SDLR in arid conditions. While challenges persist, particularly in addressing atmospheric variables and cloud masking, this work lays a foundation for accurate SLR retrieval from high spatial resolution sensors like ASTER. The potential applications extend to upcoming satellite missions, such as the Landsat Next, and contribute to advancing high-resolution remote sensing capabilities for an improved understanding of Earth's energy dynamics. [ABSTRACT FROM AUTHOR]

The increasing incidence of retrogressive thaw slumps (RTSs) in permafrost regions underscores the need for detailed spatial and temporal analysis using InSAR technology to monitor and predict dynamic changes in the process of RTSs. Nevertheless, current InSAR deformation forecasting methods employing deep learning strategies such as the traditional long short-term memory (LSTM) and recent transformer models encounter difficulties in effectively capturing temporal features. Moreover, they are limited in their ability to directly integrate spatial information. In this paper, an innovative deep learning approach named Spacetimeformer is proposed for predicting medium- and short-term InSAR deformation of RTSs in the Chumar River area. This method employs a transformer architecture with a spatiotemporal attention mechanism, which enhances the long-term prediction capabilities of time series models and dynamic spatial modeling. It is applicable to multivariate InSAR spatiotemporal deformation prediction problems. The findings include a list of 72 RTSs compiled based on derived InSAR deformation maps and Sentinel-2 optical images, of which 64 have an average deformation rate exceeding 10 mm/year, indicating signs of permafrost degradation. The density distribution of the displacement maps predicted by the Spacetimeformer model aligned well with the InSAR deformation maps obtained from the small baseline subset (SBAS) method, with the overall prediction deviation controlled within 20 mm. In addition, the point-scale prediction results were compared with LSTM and transformer models. This study indicates that the Spacetimeformer network achieved good results in predicting the deformation of RTSs, with a root mean square error of 1.249 mm. The Spacetimeformer method for deformation prediction with the spacetime mechanism presented in this study can serve as a general framework for multivariate deformation prediction based on InSAR results. It can also quantitatively assess the spatial deformation characteristics and deformation trends of RTSs. [ABSTRACT FROM AUTHOR]

Earthquake disaster risk analyses provide significant scientific guidance for reducing earthquake disaster losses etc. The current commonly used method is based on the vulnerability of different types of buildings to evaluate, and may ignore the difference between the seismic resistance of the same type of buildings in different areas. The lethal level considers a series of reasons such as different types of buildings, resulting in different capacities for casualties, the lethal levels of the cities in the same subregion are considerably different and exhibit obvious distribution characteristics. In Yancheng and Suqian, the lethal levels in urban areas are lower than those in rural areas, but the lethal levels in Guangzhou show the opposite trend (the lethal levels are higher in urban areas than in other towns and rural areas). The average lethal level in Guangzhou is 0.3856, that in Suqian is 0.5844, and that in Yancheng is 0.5912. This study based on the lethal levels, conduct a comparative analysis of the earthquake disaster risk of cities throughout eastern China, and a map of the earthquake disaster risk in each city is obtained. The overall lethal level and risk in Guangzhou is much lower than those in Yancheng and Suqian. The main influencing factors in the different cities also diverge. One reason for the lower lethal levels and risk in Guangzhou is that most of the brick-concrete structures are equipped with ring beams and structural columns; conversely, Yancheng and Suqian exhibit higher lethal levels and risk because the brick-concrete structures have ring beams but no structural columns. These maps provide a technical reference and solid scientific and technological support for further earthquake disaster risk analyses and for disaster prevention, mitigation and disaster relief planning, and this method improves the accuracy of rapid postearthquake assessment.

Osteoarthritis (OA) is a debilitating degenerative disease affecting multiple joint tissues, including cartilage, bone, synovium, and adipose tissues. OA presents diverse clinical phenotypes and distinct molecular endotypes, including inflammatory, metabolic, mechanical, genetic, and synovial variants. Consequently, innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches. Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints, causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues. This issue has led to standardization difficulties and hindered the success of clinical trials. Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues, encompassing DNA, RNA, metabolites, and proteins, as well as their chemical properties, elemental composition, and mechanical attributes, can contribute to a more comprehensive understanding of the disease subtypes. Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment, providing a more holistic view of cellular function. Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various -omics lenses, such as genomics, transcriptomics, proteomics, and metabolomics, with spatial data. This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates. Furthermore, advanced imaging techniques, including high-resolution microscopy, hyperspectral imaging, and mass spectrometry imaging, enable the visualization and analysis of the spatial distribution of biomolecules, cells, and tissues. Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes. This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis. It explores their applications, challenges, and potential opportunities in the field of OA. Additionally, this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA. [ABSTRACT FROM AUTHOR]

After an earthquake, the important task of emergency rescue work is to minimize casualties, but due to the suddenness of earthquake disasters, it is difficult to obtain enough disaster information immediately, especially personnel distribution and movement information. The traditional methods of obtaining disaster data are through reports from the disaster area or field investigations by the emergency rescue team; this work lags, and its efficiency is low. This paper analyzes the feasibility of using mobile phone location signal data in earthquake emergency rescue work in several respects, such as quantity, location, change rate, and epicentral distance. The results show that mobile phone location signal data can quickly obtain the situation of personnel distribution and quantity after an earthquake, and we find the change rate, distance, etc., can determine the approximate range of the earthquake impact field. Through the data distribution in different time periods, the movement of personnel after the earthquake can be obtained. Based on several situations, we can determine the basic situation of the disaster-stricken areas in times after the earthquake, especially the personnel relevant to the situation, and these data can provide a scientific basis for emergency rescue decision making.

Osteoarthritis (OA) remains a prevalent disease affecting more than 20% of the global population, resulting in morbidity and lower quality of life for patients. The study of OA pathophysiology remains predominantly in animal models due to the complexities of mimicking the physiological environment surrounding the joint tissue. Recent development in microfluidic organ-on-chip (OoC) systems have demonstrated various techniques to mimic and modulate tissue physiological environments. Adaptations of these techniques have demonstrated success in capturing a joint tissue's tissue physiology for studying the mechanism of OA. Adapting these techniques and strategies can help create human-specific in vitro models that recapitulate the cellular processes involved in OA. This review aims to comprehensively summarise various demonstrations of microfluidic platforms in mimicking joint microenvironments for future platform design iterations. [ABSTRACT FROM AUTHOR]

Identifying building function type (BFT) is vital for many studies and applications, such as urban planning, disaster risk assessment and management, and traffic control. Traditional remote sensing methods are commonly used for land use/cover classification, but they have some limitations in BFT identification. Considering that the dynamic variations of social sensing mobile signaling (MS) data at diurnal and daily scales are directly related to BFT, in this paper, we propose a method to infer BFT using MS data obtained from mobile devices. First, based on the different patterns of population dynamics within different building types, we propose a BFT classification scheme with five categories: residential (R), working (W), entertainment (E), visiting (V), and hospital (H). Then, a random forest (RF) classification model is constructed based on two days (one workday and one weekend) of MS data with a temporal resolution of one hour to identify the BFT. According to the cross-validation method, the overall classification accuracy is 84.89%, and the Kappa coefficient is 0.78. Applying the MS data-constructed RF model to the central areas of Beijing Dongcheng and Xicheng Districts, the overall detection rate is 97.35%. In addition, to verify the feasibility of the MS data, the Sentinel-2 (S2) remote sensing data are used for comparison, with a classification accuracy of 73.33%. The better performance of the MS method shows its excellent potential for BFT identification, as the spatial and temporal population dynamics reviewed based on MS data are more correlated with BFT than geometric or spectral features in remote sensing images. This is an innovative attempt to identify BFT with MS data, and such a method compensates for the scarcity of BFT studies driven by population dynamics. Overall, in this study, we show the feasibility of using time series MS data to identify BFT and we provide a new path for building function mapping at large scales. [ABSTRACT FROM AUTHOR]

The calcified cartilage zone (CCZ) is a thin interlayer between the hyaline articular cartilage and the subchondral bone and plays an important role in maintaining the joint homeostasis by providing biological and mechanical support from unmineralized cartilage to the underlying mineralized subchondral bone. The hallmark of CCZ characteristics in osteoarthritis (OA) is less well known. The aim of our study is to evaluate the structural, molecular, and biochemical composition of CCZ in tissues affected by primary knee OA and its relationship with disease severity. We collected osteochondral tissue samples stratified according to disease severity, from 16 knee OA patients who underwent knee replacement surgery. We also used meniscectomy‐induced rat samples to confirm the pathophysiologic changes of human samples. We defined the characteristics of the calcified cartilage layer using a combination of morphological, biochemical, proteomic analyses on laser micro‐dissected tissue. Our results demonstrated that the Calcium/Phosphate ratio is unchanged during the OA progression, but the calcium‐binding protein and cadherin binding protein, as well as carbohydrate metabolism‐related proteins, undergo significant changes. These changes were further accompanied by thinning of the CCZ, loss of collagen and proteoglycan content, the occurrence of the endochondral ossification, neovasculature, loss of the elastic module, loss of the collagen direction, and increase of the tortuosity indicating an altered structural and mechanical properties of the CCZ in OA. In conclusion, our results suggest that the calcified cartilage changes can reflect the disease progression. [ABSTRACT FROM AUTHOR]

Considering the significant influence of clouds on thermal infrared (TIR) data sea surface temperature (SST) retrieval, this study focuses on the reduction of cloud influence on Moderate Resolution Imaging Spectroradiometer (MODIS) longwave SST retrieval. First, the quality level (QL) 3 MODIS SSTs are classified into cloudy or clear-sky pixels based on MODIS cloud mask products. The cloudy SST pixels, flagged as confident cloudy or probably cloudy in the cloud masks, are further classified into three groups according to their associated daytime cloud-top and optical properties. Taking the SSTs measured by 11 buoys over one year as the reference data, those three groups of cloudy SSTs are significantly underestimated, with biases of −33.45 °C, −6.35 °C, and −4.72 °C. The QL 3 clear-sky SSTs are identified as probably and confident clear in the cloud masks and are not influenced by clouds, with a bias of nearly 0 °C. Then, three support vector regression (SVR) models are individually proposed for the three groups of cloudy SSTs. Taking the cloud-top and optical parameters as inputs of the proposed SVR models, we can obtain SSTs with a bias of 0 °C and a root-mean-square error (RMSE) of less than 1.6 °C for the three groups of cloudy SSTs. The method proposed in this study shows the potential for TIR SST estimation under some cloudy conditions using satellite remote sensing cloud products. Considering the RMSE of 0.4 °C in operational sea surface temperature and sea ice analysis, further study is needed before actual applying the method proposed in this study. [ABSTRACT FROM AUTHOR]

This article focuses on the rapid assessment of possible numbers of casualties after an earthquake. We use data on earthquake casualties to study and verify the relationship between casualties and building types, building anti-lethal levels, seismic intensity and other influencing factors. A rapid assessment method for casualties based on buildings' anti-lethal level is proposed. The method first uses the anti-lethal level and proportions of various types of buildings to obtain the overall anti-lethal level of the study area. Second, according to historical casualty data, the casualty rate of each intensity in the study area is obtained. On this basis, a rapid assessment matrix of casualties is constructed based on the overall anti-lethal level of the study area and the casualty rate of each intensity. Finally, we make a rapid assessment of casualties based on a matrix model. Through the verification of 7 selected historical earthquakes, we find that the data on casualties obtained based on this method are close to the actual number of casualties. The resulting casualty parameters are described against the overall casualty data from several historical earthquakes in China, and the error rates of earthquakes in Lancang-Gengma, Yushu, Lushan Min county-Zhang county and Changning are 4.1%, 25.27%, 12.75%, 2% and 7.69%, respectively. The error is relatively small, within 50%, and a more accurate evaluation result is obtained. Our findings illustrate the effectiveness and practicability of a rapid assessment method for casualties based on anti-lethal levels. [ABSTRACT FROM AUTHOR]

After earthquake, the correct and efficient earthquake emergency evacuation of pedestrian is crucial for reducing casualties. Based on the research on the quantitative of panic coefficient and the calibration of initial desired speed, this research has constructed the revised social force model which could accurately simulate the post-earthquake evacuation process of the crowed. Using the revised social force model, the effects of classroom layout, seismic intensity, the number of safeties exits and the number of students in the classroom on the evacuation efficiency are further studied. And then, according to the research results, the classroom layout has been further optimized to improve the students' earthquake emergency evacuation efficiency. These research results are scientific bases for the targeted earthquake emergency safety education and the design of classroom layout. [ABSTRACT FROM AUTHOR]

After an earthquake, rapidly assessing the affected areas, particularly the meizoseismal area, is crucial to emergency rescue work. Currently, many methods exist for delimiting the range of the area affected by an earthquake. The main method for estimating this area is to use an empirical model of the seismic intensity attenuation relationship. In contrast, this article uses the change rate (CR) of the phones' location data in an area after the earthquake to infer the extent of the meizoseismal area. This study selects phone location data from eight earthquakes for experimentation and analysis. The results show a correlation between the phone location data and the actual distribution of the meizoseismal area based on intensity maps that were issued by the China Earthquake Administration. According to the results, the extent of the meizoseismal area can be estimated by the magnitude, the focal depth, the CR, the time of the earthquake, and the distance to the epicenter. The magnitude, CR, and focal depth are dominant and are positively correlated with the intensity. Based on the aforementioned parameters, a calculation model for delimiting the extent of the meizoseismal area based on phone location data can be constructed. The accuracy of the correlation coefficient of fitting result is 0.7826, and the R² value is 0.6125. The results demonstrate that phone location data can be used to quickly delimit the meizoseismal area and can provide a basis for earthquake emergency rescue work. [ABSTRACT FROM AUTHOR]

On the basis of data obtained from China Earthquake Administration and China National Statistics Bureau, this paper focuses on the earthquake disaster loss characteristics in 31 provinces or municipalities of China from 1993 to 2016. The trends of direct economic losses, casualties and affected population are also analyzed by constructing two relative earthquake loss indexes (losses/GDP and losses/E) and employing gray correlation theory. Results show that during the 24 years, without taking Wenchuan Ms8.0 earthquake into account, the direct economic losses exhibit a significant increasing trend while losses/GDP also increases but is not significant. Meanwhile, the injuries and affected population both witness a decline trend but the fatalities show the opposite. According to the correlational degree of each year, 8 years (1995, 1996, 1998, 2003, 2008, 2010, 2013 and 2014) suffered losses of equal or above moderate grade. Regionally, the western China suffered the most accumulative direct economic losses and losses/GDP. However, the ranking results of losses/E demonstrate that the central China is the most vulnerable when confronting with the same earthquake. Specifically, Sichuan and Yunnan suffered losses of extra-large grade, Qinghai of large grade and Tibet, Xinjiang and Guangdong of moderate grade. These discoveries are helpful for earthquake mitigation and preparedness as well as earthquake loss evaluation in China. [ABSTRACT FROM AUTHOR]

With the booming development of evacuation simulation software, developing an extensive database in indoor scenarios for evacuation models is imperative. In this paper, we conduct a qualitative and quantitative analysis of the collected videotapes and aim to provide a complete and unitary database of pedestrians’ earthquake emergency response behaviors in indoor scenarios, including human-environment interactions. Using the qualitative analysis method, we extract keyword groups and keywords that code the response modes of pedestrians and construct a general decision flowchart using chronological organization. Using the quantitative analysis method, we analyze data on the delay time, evacuation speed, evacuation route and emergency exit choices. Furthermore, we study the effect of classroom layout on emergency evacuation. The database for indoor scenarios provides reliable input parameters and allows the construction of real and effective constraints for use in software and mathematical models. The database can also be used to validate the accuracy of evacuation models. [ABSTRACT FROM AUTHOR]

Optimal feeding strategies improve fish growth and health but may be affected by ammonia stress in closed rearing systems such as tanks or ponds. This study aimed to evaluate the effects of feeding frequency and ammonia levels in rearing water on the enzymes and genes involved in oxidative stress of yellow catfish. Experiment (ammonia exposure) and control groups were randomly assigned to one of three feeding frequencies (1, 2 and 4 times daily) for 8 weeks. Weight gain increased as feeding frequency increased from 1 to 4 times daily, but feed conversion ratio values decreased. The highest survival in ammonia group was found when fish was fed 2 times daily. Glutathione peroxidase activity and total antioxidant capacity in liver and brain of fish exposed to ammonia increased as feeding frequency increased from 1 to 4 times daily. Liver malondialdehyde content in control group decreased as feeding frequency increased from 1 to 4 times daily. The lowest liver malondialdehyde content in ammonia group was observed when fish was fed 2 times daily. Liver mRNA expression of superoxide dismutase and catalase in control group increased as feeding frequency increased from 1 to 4 times daily, but the highest superoxide dismutase and catalase expression in ammonia group were observed when fish were fed 2 times daily. This study indicates that higher feeding frequency of yellow catfish exposed to ammonia could result in oxidative stress and poor survival. The optimal feeding frequency of yellow catfish exposed to ammonia is 2 times daily. [ABSTRACT FROM AUTHOR]

Thin cirrus clouds are dominated by non-spherical ice crystals with an effective emissivity of less than 0.5. Until now, the influences of clouds were not commonly considered in the development of algorithms for retrieving land-surface temperature (LST). However, numerical simulations showed that the influence of thin cirrus clouds could lead to a maximum LST retrieval error of more than 14 K at night if the cirrus optical depth (COD) at 12 μm was equal to 0.7 (cirrus emissivity equivalent to 0.5). To obtain an accurate estimate of the LST under thin cirrus using satellite infrared data, a nonlinear three-channel LST retrieval algorithm was proposed based on a widely used two-channel algorithm for clear-sky conditions. The variations in the cloud top height, COD, and effective radius of cirrus clouds were considered in this three-channel LST retrieval algorithm. Using Moderate Resolution Imaging Spectroradiometer (MODIS) channels 20, 31, and 32 (centred at 3.8, 11.0, and 12.0 μm, respectively) and the corresponding land surface emissivities (LSEs), the simulated data showed that this algorithm could obtain LSTs with root mean square errors (RMSEs) of less than 2.8 K when the COD at 12 μm is less than 0.7 and the viewing zenith angle (VZA) is less than 60°. In addition, a sensitivity analysis of the proposed algorithm showed that the total LST errors, including errors from the uncertainties in input parameters and algorithm error, were nearly the same as the algorithm error itself. Some lake surface water temperatures measured in Lake Superior and Lake Erie were used to test the performance of the proposed LST retrieval algorithm. The results showed that the proposed nonlinear three-channel algorithm could be used for estimating LST under thin cirrus with an RMSE of less than 2.8 K. [ABSTRACT FROM PUBLISHER]

It is worth noting that the influences of dust aerosol type and different aerosol loadings were not considered in the development of the generalized split-window (GSW) algorithm. However, numerical simulations showed that the influence of dust aerosol could lead to a maximum land surface temperature (LST) retrieval error of 5.12 K when the aerosol optical depth (AOD) in the atmosphere is 1.0 and viewing zenith angle (VZA) is 60°. This paper focuses on reducing the influence of dust aerosol on the LST retrieval error of the GSW algorithm. A linear function was developed to reduce such influence with respect to the AOD. The slope could be expressed as a function of the difference between the MODIS channel brightness temperatures \bmT\bf{31} and \bmT\bf{32} measured at the top of the atmosphere (TOA) and difference and mean of the two-channel emissivities, and the offset could be used as a constant value for each VZA. The results showed that the retrieval accuracy could be improved by approximately 4 K for \bfAOD = \bf1.0 and \bfVZA = \bf60^\circ. Sensitivity analysis in terms of the uncertainties of the input parameters showed that the maximum LST retrieval error is 1.15 K for \bfVZA = \bf0^\circ. Some of the in situ measurements observed at the Yingke site in northwest China and Arvaikheer site in south Mongolia were used to test the proposed method, respectively. The results showed that the proposed method could improve the LST retrieval accuracy by at least 1 K for the GSW algorithm in atmospheres with heavy dust aerosol loading. [ABSTRACT FROM PUBLISHER]

Background: Enzymes are central components of many physiological processes, and changes in enzyme activity are linked to numerous disease states, including osteoarthritis (OA). Assessing changes in enzyme function can be challenging because of difficulties in separating affected tissue areas that result in the homogenisation of healthy and diseased cells. Direct correlation between spatially-resolved enzyme distribution(s) and diseased cells/tissues can thus lead to advances in our understanding of OA pathophysiology. Herein, we present a method that uses mass spectrometry imaging (MSI) to visualise the distribution of lipase enzymes and their downstream lipid products in fresh bone and cartilage tissue sections. Immunohistostaining of adjacent tissue sections was then used to identify OA cells/tissues, which were then statistically correlated with molecular-level images. Methods: MSI was used to image lipase enzymes, their substrates, and their metabolic products to validate enzymatic activity and correlate to OA regions determined by immunohistochemistry (IHC). Based on the modified Mankin score, six non-OA and OA patient-matched osteochondral samples were analysed by matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI). Due to the involvement of phospholipase A2 (PLA 2 ) in inflammatory pathways, explant tissues were treated with IL-1β to mimic inflammation observed in OA. Bovine explant tissues were then subject to MSI methods to observe the spatial distribution of PLA 2 . Results: Compared with non-OA samples, OA samples showed an elevated level of multiple arachidonic acid (AA)-containing phospholipids ( P < 0.001), in which the elevation in the surface and deep layer cartilage of OA tissues is correlated to elevated PLA 2 activity ( P < 0.001). Bovine explant tissues treated with IL-1β to mimic OA pathophysiology validated these results and displayed elevated PLA 2 levels in OA mimic samples relative to the controls ( P < 0.001). It was established that the PLA 2 G 2 A isoform specifically was responsible for PLA 2 enzyme activity changes in OA tissues ( P < 0.001). Conclusion: Our results present a reliable method for imaging enzyme dynamics in OA cartilage, which sets up the foundation for future spatial enzyme dynamics in the OA field. We demonstrated that OA patients exhibit increased expression of PLA 2 G 2 A at the superficial and deep cartilage zone that degrades cartilage differently at the spatial level. A tissue-specific PLA 2 G 2 A precision inhibition may be the potential target for OA., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Osteoarthritis is the most prevalent degenerative joint disease and one of the leading causes of physical impairment in the world's aging population. The human lifespan has significantly increased as a result of scientific and technological advancements. According to estimates, the world's elderly population will increase by 20% by 2050. Aging and age-related changes are discussed in this review in relation to the development of OA. We specifically discussed the cellular and molecular changes that occur in the chondrocytes during aging and how these changes may make synovial joints more susceptible to OA development. These changes include chondrocyte senescence, mitochondrial dysfunction, epigenetic modifications, and decreased growth factor response. The age-associated changes occur not only in the chondrocytes but also in the matrix, subchondral bone, and synovium. This review aims to provide an overview of the interplay between chondrocytes and matrix and how age-related changes affect the normal function of cartilage and contribute to OA development. Understanding the alterations that affect the function of chondrocytes will emerge new possibilities for prospective therapeutic options for the treatment of OA.

Articular cartilage function depends on the temporal and zonal distribution of coordinated metabolic regulation in chondrocytes. Emerging evidence shows the importance of cellular metabolism in the molecular control of the cartilage and its dysregulation in degenerative diseases like osteoarthritis (OA). Compared to most other tissues, chondrocytes are sparsely located in the extracellular matrix, lacking the typical proximity of neural, vascular, and lymphatic tissue. Making up under 5% of the total tissue weight of cartilage, chondrocytes have a relative deficiency of access to nutrients and oxygen, as well as limited pathways for metabolite removal. This makes cartilage a unique tissue with hypocellularity, prolonged metabolic rate, and tissue turnover. Studies in the past decade have shown that several pathways of central carbon metabolism are essential for cartilage homeostasis. Here, we summarised the literature findings on the role of cellular metabolism in determining the chondrocyte function and how this metabolic dysregulation led to cartilage aging in OA and provided an outlook on how the field may evolve in the coming years. Although the various energy metabolism pathways are inextricably linked with one another, for the purpose of this review, we initially endeavoured to examine them individually and in relative isolation. Subsequently, we comment on what is known regarding the integration and linked signalling pathways between these systems and the therapeutic opportunities for targeting OA metabolism., Competing Interests: Conflicts of interest The authors have no conflicts of interest to declare., (copyright: © 2022 Wu et al.)

Osteoarthritis (OA) is a long-term condition that causes joint pain and reduced movement. Notably, the same pathways governing cell growth, death, and differentiation during the growth and development of the body are also common drivers of OA. The osteochondral interface is a vital structure located between hyaline cartilage and subchondral bone. It plays a critical role in maintaining the physical and biological function, conveying joint mechanical stress, maintaining chondral microenvironment, as well as crosstalk and substance exchange through the osteochondral unit. In this review, we summarized the progress in research concerning the area of osteochondral junction, including its pathophysiological changes, molecular interactions, and signaling pathways that are related to the ultrastructure change. Multiple potential treatment options were also discussed in this review. A thorough understanding of these biological changes and molecular mechanisms in the pathologic process will advance our understanding of OA progression, and inform the development of effective therapeutics targeting OA., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Fan, Wu, Crawford, Xiao and Prasadam.)

With the booming development of evacuation simulation software, developing an extensive database in indoor scenarios for evacuation models is imperative. In this paper, we conduct a qualitative and quantitative analysis of the collected videotapes and aim to provide a complete and unitary database of pedestrians' earthquake emergency response behaviors in indoor scenarios, including human-environment interactions. Using the qualitative analysis method, we extract keyword groups and keywords that code the response modes of pedestrians and construct a general decision flowchart using chronological organization. Using the quantitative analysis method, we analyze data on the delay time, evacuation speed, evacuation route and emergency exit choices. Furthermore, we study the effect of classroom layout on emergency evacuation. The database for indoor scenarios provides reliable input parameters and allows the construction of real and effective constraints for use in software and mathematical models. The database can also be used to validate the accuracy of evacuation models., Competing Interests: The authors have declared that no competing interests exist.

Studies indicated that a root mean square error (RMSE) of 3.7 K was found if dust aerosol was not considered in the traditional land surface temperature (LST) retrieval algorithm. To reduce the influence of dust aerosol on LST estimation, a three-channel algorithm is proposed using MODIS channels 29, 31, and 32 with model coefficients irrelevant to the aerosol optical depth (AOD). Compared with actual and estimated LSTs, the RMSEs are 1.8 K and 1.6 K for dry and wet atmospheres, respectively, when the AOD is 1.0. Sensitivity analyses considering instrument noise, land surface emissivity uncertainties, and the algorithm error itself show that the LST errors are 2.5 K and 1.7 K for dry and wet atmospheres, respectively, when the AOD is 1.0. Finally, some in situ measured LSTs at the Jichanghuangmo, Huazhaizi, and Yingke sites in northwest China are taken as referenced LST values and compared with the MODIS LST products MOD11&#95;L2/MYD11&#95;L2 and those estimated with the proposed method. The results show that the proposed method can improve the LST retrieval accuracy from 1.4 K to 2.2 K in dust aerosol atmospheres.

Simulated data showed that cirrus clouds could lead to a maximum land surface temperature (LST) retrieval error of 11.0 K when using the generalized split-window (GSW) algorithm with a cirrus optical depth (COD) at 0.55 μm of 0.4 and in nadir view. A correction term in the COD linear function was added to the GSW algorithm to extend the GSW algorithm to cirrus cloudy conditions. The COD was acquired by a look up table of the isolated cirrus bidirectional reflectance at 0.55 μm. Additionally, the slope k of the linear function was expressed as a multiple linear model of the top of the atmospheric brightness temperatures of MODIS channels 31-34 and as the difference between split-window channel emissivities. The simulated data showed that the LST error could be reduced from 11.0 to 2.2 K. The sensitivity analysis indicated that the total errors from all the uncertainties of input parameters, extension algorithm accuracy, and GSW algorithm accuracy were less than 2.5 K in nadir view. Finally, the Great Lakes surface water temperatures measured by buoys showed that the retrieval accuracy of the GSW algorithm was improved by at least 1.5 K using the proposed extension algorithm for cirrus skies.