A novel marker integrating multiple genetic alterations better predicts platinum sensitivity in ovarian cancer than HRD score

Introduction: Platinum-based chemotherapy is the first-line treatment strategy for ovarian cancer patients. The dismal prognosis of ovarian cancer was shown to be stringently associated with the heterogeneity of tumor cells in response to this therapy, therefore understanding platinum sensitivity in ovarian cancer would be helpful for improving patients’ quality of life and clinical outcomes. HRDetect, utilized to characterize patients’ homologous recombination repair deficiency, was used to predict patients’ response to platinum-based chemotherapy. However, whether each of the single features contributing to HRD score is associated with platinum sensitivity remains elusive.Methods: We analyzed the whole-exome sequencing data of 196 patients who received platinum-based chemotherapy from the TCGA database. Genetic features were determined individually to see if they could indicate patients’ response to platinum-based chemotherapy and prognosis, then integrated into a Pt-score employing LASSO regression model to assess its predictive performance.Results and discussion: Multiple genetic features, including bi-allelic inactivation of BRCA1/2 genes and genes involved in HR pathway, multiple somatic mutations in genes involved in DNA damage repair (DDR), and previously reported HRD-related features, were found to be stringently associated with platinum sensitivity and improved prognosis. Higher contributions of mutational signature SBS39 or ID6 predicted improved overall survival. Besides, arm-level loss of heterozygosity (LOH) of either chr4p or chr5q predicted significantly better disease-free survival. Notably, some of these features were found independent of HRD. And SBS3, an HRD-related feature, was found irrelevant to platinum sensitivity. Integrated all candidate markers using the LASSO model to yield a Pt-score, which showed better predictive ability compared to HRDetect in determining platinum sensitivity and predicting patients’ prognosis, and this performance was validated in an independent cohort. The outcomes of our study will be instrumental in devising effective strategies for treating ovarian cancer with platinum-based chemotherapy

Research on lethal levels of buildings based on historical seismic data

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

Daytime Land Surface Temperature Extraction from MODIS Thermal Infrared Data under Cirrus Clouds

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

A Thermal Infrared Land Surface Temperature Retrieval Algorithm for Thin Cirrus Skies Using Cirrus Optical Properties

To acquire daytime land surface temperature (LST) in thin cirrus cloudy skies, we have developed a three-channel LST retrieval algorithm based on a widely used two-channel LST retrieval algorithm for the clear-sky conditions. In this algorithm, the LST is expressed as a multiple linear function of MODIS channels 29, 31 and 32 with the coefficients of the linear function dependent on the cirrus optical depth (COD) and cirrus effective radius (R). The influences from land surface emissivities (LSEs) are also considered in this algorithm. The simulated dataset shows that the LST could be estimated using the proposed algorithm with the root mean squire error (RMSE) less than 2.2 K in thin cirrus cloudy skies (COD less than 0.7) when viewing zenith angle (VZA) equivalent to 0°. As VZA is equivalent to 60°, the maximum RMSE are 2.7 K. The widely used generalized split-window (GSW) algorithm proposed for clear-sky conditions are used in cirrus cloudy skies, and the RMSEs of GSW algorithm estimated LST are 16.89 K and 22.32 K for VZA =0° and VZA =60° respectively when COD is 0.7. It indicates that the proposed three-channel algorithm can significantly improve the LST retrieval accuracy using thermal infrared data in cirrus cloudy skies. To estimate the LST errors caused by the uncertainties of COD, R, LSE and instrument noise, a sensitivity analysis was performed. It shows that the accuracy of cirrus COD is more important for the retrieval of LST compared with other parameters. The maximum total LST errors, taking into account all the input parameters’ uncertainty and algorithm error itself, are 3.8 K and 4.3 K when VZA =0° and VZA =60° respectively

Macro, Micro, and Molecular. Changes of the Osteochondral Interface in Osteoarthritis Development

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.</p

Building Function Type Identification Using Mobile Signaling Data Based on a Machine Learning Method

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

Research on the application of mobile phone location signal data in earthquake emergency work: A case study of Jiuzhaigou earthquake.

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

The Metabolic Landscape in Osteoarthritis

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

Building Function Type Identification Using Mobile Signaling Data Based on a Machine Learning Method

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

Prediction of essential proteins based on subcellular localization and gene expression correlation

Abstract Background Essential proteins are indispensable to the survival and development process of living organisms. To understand the functional mechanisms of essential proteins, which can be applied to the analysis of disease and design of drugs, it is important to identify essential proteins from a set of proteins first. As traditional experimental methods designed to test out essential proteins are usually expensive and laborious, computational methods, which utilize biological and topological features of proteins, have attracted more attention in recent years. Protein-protein interaction networks, together with other biological data, have been explored to improve the performance of essential protein prediction. Results The proposed method SCP is evaluated on Saccharomyces cerevisiae datasets and compared with five other methods. The results show that our method SCP outperforms the other five methods in terms of accuracy of essential protein prediction. Conclusions In this paper, we propose a novel algorithm named SCP, which combines the ranking by a modified PageRank algorithm based on subcellular compartments information, with the ranking by Pearson correlation coefficient (PCC) calculated from gene expression data. Experiments show that subcellular localization information is promising in boosting essential protein prediction