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Abstract The distribution of industrial heat sources (IHSs) is a crucial indicator for evaluating energy consumption and air pollution levels. However, there is a notable lack of IHS datasets in China that are frequently updated, span long periods, contain detailed characteristic information, have been individually validated and are publicly available. In this study, IHS datasets from China between 2012 and 2021 were constructed using the Visible Infrared Imaging Radiometer Suite (VIIRS) I Band 375 m NRT Active Fire/Hotspots (ACF) Product (VNP14IMGTDL_NRT) to monitor and analyse large‐scale IHSs. First, a density segmentation method based on an improved K‐means algorithm using ACF data and spatial topological correlation analysis was conducted to construct the IHS. Then, 4410 records covering China between 2012 and 2021, with 21 attributes, were obtained and verified, with an individual identification precision of 95.08% via manual verification based on high‐resolution remote‐sensing images and point of interest (POI) data. Finally, the trend of the spatiotemporal variation in IHSs was analysed using a long time series. The results showed that the spatial distribution of IHSs in China from 2012 to 2021 exhibited local aggregation and a gradual shift from east to west. In addition, the number of IHSs in China showed an initial increasing trend from 2012 to 2014, followed by a decrease since 2014, consistent with national energy reform‐related policies. The results of this study indicate the temporal variation in IHSs, enhance the precision of identifying fire location categories and demonstrate the potential for improving energy efficiency, reducing emissions and ensuring sustainable development in China.

Abstract The spatiotemporal distribution of industrial heat sources (IHS) is an important indicator for assessing levels of energy consumption and air pollution. Continuous, comprehensive, dynamic monitoring and publicly available datasets of global IHS (GIHS) are lacking and urgently needed. In this study, we built the first long-term (2012–2021) GIHS dataset based on the density-based spatiotemporal clustering method using multi-sources remote sensing data. A total of 25,544 IHS objects with 19 characteristics are identified and validated individually using high-resolution remote sensing images and point of interest (POI) data. The results show that the user’s accuracy of the GIHS dataset ranges from 90.95% to 93.46%, surpassing other global IHS products in terms of accuracy, omission rates, and granularity. This long-term GIHS dataset serves as a valuable resource for understanding global environmental changes and making informed policy decisions. Its availability contributes to filling the gap in GIHS data and enhances our knowledge of global-scale industrial heat sources.

Evaluation the value of ecosystem services can provide crucial reference for formulating ecological protection plans and making informed management decision. This study utilized Landsat remote sensing images as the data source and employed land-use transfer chains and land use transfer matrix methods to compare the ecological effects of land use and land cover change(LUCC) in the two typical ecological functional zones of Egypt, one area isthe northern region of Egypt with the Priority function of Economic Development, another area is the Nasser Reservoir area with the priority function of water conservation. The key findings are as follows: (1)The northern region of Egypt and the Nasser Reservoir exhibited different trends across the ecological functional zones.From 1985 to 2020, the northern region of Egypt is characterized by an increase in arable land and impervious surface,the Nasser Reservoir area is distinguished by the expansion of water and a reduction in sand.(2) Key land transfer patterns vary significantly between the ecological functional zones of the northern region of Egypt and the Nasser Reservoir. From 1985 to 2020, the Egypt’s northern region experienced significant shifts between arable land, sand, and impervious surfaces. The conversion of arable land to impervious surfaces represented 2.41% of the total arable land transfer area, while sand converted to impervious surfaces accounted for 11.68% of the total sand transfer area. In the Nasser Reservoir region, significant conversions occurred between water, grasslands, and sand. Sand converted to water made up 3.83% of the total sand transfer area, while grassland converted to water compriseed 5.38% of the total grassland transfer area. (3) The ecological benefits of the northern region of Egypt and the Nasser Reservoir have increased significantly. In northern region of Egypt, the primary drivers of this increase in ecological service value are the conversion of arable land and grasslands, along with the ecological development and utilization of sand. In the Nasser Reservoir area,the inflow of water and the continued outflow of sands are the main factors contributing to the rise in ecosystem service value. (4) Differentiated LUCC management should be implemented for different ecological functional zones. In northern region of Egypt, the focus should be on balancing ecological security with human development, while the Nasser Reservoir area should prioritize water resource protection and sustainable development. The research findings are not only significant for optimizing LUCC but also for enhancing ecosystem service functions in Egypt.

Abstract Objective This retrospective study aimed to assess the impact of hysteroscopic septum incision on in vitro fertilization (IVF) outcomes among infertile women diagnosed with a complete septate uterus and no history of recurrent pregnancy loss. Methods The study was conducted at a tertiary reproductive center affiliated with a university hospital and involved 78 women with a complete septate uterus. Among them, 34 women underwent hysteroscopic septum incision, while 44 women opted for expectant management. The primary outcome measure was the live birth rate, while secondary outcomes included clinical pregnancy rate, preterm birth rate, miscarriage rate, and ongoing pregnancy rate. Results Women who underwent hysteroscopic septum incision demonstrated a comparable likelihood of achieving a live birth compared to those managed expectantly (25% vs. 25%, Relative Risk (RR): 1.000, 95% Confidence Interval (CI): 0.822 to 1.216). No preterm births occurred in either group. The clinical pregnancy rate, ongoing pregnancy rate, and miscarriage rate showed no significant differences between the surgical group and the expectant management group. Subgroup analyses based on the type of embryo transferred also revealed no significant differences in outcomes. Conclusions Hysteroscopic septum incision does not appear to yield improved IVF outcomes compared to expectant management in infertile women with a complete septate uterus and no history of recurrent pregnancy loss.

Arid oasis ecosystems are susceptible and fragile ecosystems on Earth. Studying the interaction between deserts, oases, and impervious surfaces is an essential breakthrough for the harmonious and sustainable development of people and land in drylands. Based on gridded data such as land use and NDVI, this article analyzes the interaction characteristics between oases, deserts, and impervious surfaces in northern Egypt and examines their dynamics using modeling and geographic information mapping methods. The results show the following: In terms of the interaction between deserts and oases, the primary manifestation was the expansion of oases and the reduction of deserts. During the study period, the oases in the Nile Delta and Fayoum District increased significantly, with the area of oases in 2020 being 1.19 times the area in 2000, which shows a clear trend of advance of people and retreat of sand. The interaction between oases and impervious surfaces was mainly observed in the form of the spread of impervious surfaces on arable land into oases. During the study period, the area of impervious surfaces increased 2.32 times. The impervious surface expanded over 1903.70 km2 of arable land, accounting for 66.67% of the expanded area. The central phenomenon between the impervious surface and the desert was the encroachment of the covered area of the impervious surface into the desert, especially around the city of Cairo. Population growth and urbanization are the two central drivers between northern Egypt’s oases, deserts, and impervious surfaces. The need for increased food production due to population growth has forced oases to move deeper into the desert, and occupation of arable land due to urbanization has led to increasing pressure on arable land, creating a pressure-conducting dynamic mechanism. Finally, countermeasures for sustainable regional development are suggested.

ObjectiveTo determine whether the late-follicular-phase progesterone to retrieved oocytes (P/O) ratio during in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) impacts pregnancy outcomes.Design12,874 cycles were retrospectively categorized into four groups according to the P/O ratio percentile, with divisions at the 25th, 50th and 75th percentiles.ResultsThe clinical pregnancy and live birth rates of fresh cycle embryos in Group D were significantly lower than those in the other three groups (45.1% and 39.0%, 43.2% and 37.2%, 39.6% and 33.5%, 33.4% and 28.2% in Group A, B, C, D, respectively; both P < 0.008). Multivariate logistic regression analysis revealed a significant negative correlation between the P/O ratio and live birth, particularly when the P/O ratio was ≥0.22 (OR = 0.862, 95% CI [0.774–0.959], P = 0.006).ConclusionsThe P/O ratio has certain predictive value for IVF/ICSI pregnancy outcomes and can be used for decision-making decision regarding fresh embryo transfer.

Land use change has a significant impact on the sustainability of ecosystems, and ecological security patterns (ESPs) can improve environmental quality through spatial planning. This study explored a multi-scenario ESP framework by integrating future land use simulation (FLUS) and minimum cumulative resistance (MCR) for urban agglomeration along the Yellow River Basin (YRB) in Ningxia. The research involved simulating land use change in 2035 under four development scenarios, identifying ecological security networks, and evaluating network stability for each scenario. The study revealed that the ecological sources under different development scenarios, including a natural development scenario (NDS), an economic development scenario (EDS), a food security scenario (FSS), and an ecological protection scenario (EPS), were 834.82 km2, 715.46 km2, 785.56 km2, and 1091.43 km2, respectively. The overall connectivity values (OG) for these scenarios were 0.351, 0.466, 0.334, and 0.520, respectively. It was found that under an EPS, the ESPs had the largest area of ecological sources and the most stable ecological network structure, which can effectively protect natural habitats. This study provides a valuable method for identifying ESPs that can respond to diversity and the uncertainty of future development. It can assist decision-makers in enhancing the ecological quality of the study area while considering various development scenarios.

Industrial heat sources (IHSs) are key contributors to anthropogenic heat, air pollution, and carbon emissions. Accurately and automatically detecting their production areas (IHSPAs) on a large scale is vital for environmental monitoring and decision making, yet this is challenged by the lack of high-resolution thermal data. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data with the highest resolution (30 m) in the civilian field and a three-band advantage were first introduced to detect IHSPAs. In this study, an IHSPA identification model using multi-features extracted from SDGSAT-1 TIS and Landsat OLI data and support vector machine (SVM) was proposed. First, three brightness temperatures and four thermal radiation indices using SDGSAT-1 TIS and Landsat OLI data were designed to enlarge the temperature difference between IHSPAs and the background. Then, 10 features combined with three indices from Landsat OLI images with the same spatial resolution (30 m) and stable data were extracted. Second, an IHSPA identification model based on SVM and multi-feature extraction was constructed to identify IHSPAs. Finally, the IHS objects were manually delineated and verified using the identified IHSPAs and Google Earth images. Some conclusions were obtained from different comparisons in Wuhai, China: (1) IHSPA identification based on SVM using thermal and optical features can detect IHSPAs and obtain the best results compared with different features and identification models. (2) The importance of using thermal features from the SDGSAT-1 TIS to detect IHSPAs was demonstrated by different importance analysis methods. (3) Our proposed method can detect more IHSs, with greater spatial coverage and smaller areas, compared with the methods of Ma and Liu. This new way to detect IHSPAs can obtain higher-spatial-resolution emissions of IHSs on a large scale and help decision makers target environmental monitoring, management, and decision making in industrial plant processing.

PurposeTo investigate the oncologic outcomes of patients with early-stage endometrioid endometrial cancer (EEC) treated with in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) following fertility-sparing treatment (FST).MethodsA total of 62 patients who underwent IVF/ICSI treatment in a single fertility center between June 2010 and December 2021 after conservative treatment for early-stage EEC were assigned to a recurrence group and a non-recurrence group. Their clinical characteristics and disease outcomes were retrospectively evaluated.ResultsThe 62 women with complete remission (CR) after FST for EEC underwent 103 IVF cycles, resulting in 41 fresh embryo transfers (ETs) and 70 frozen–thawed transfers; 27 (43.55%) achieved clinical pregnancies, and 20 (32.26%) gave birth to a total of 23 live neonates. Additionally, nine patients had live births from natural pregnancies after IVF failure, bringing the cumulative live birth rate to 46.77% (29/62). After a median follow-up period of 53.88 months (range 20.2–127.5 months), 17 patients (27.42%) experienced recurrence within 2.8 to 57.9 months after the first controlled ovarian stimulation (COS). The probability of relapse at 1, 2, and 3 years after the initiation of COS was 14.52% (9/62), 21% (13/62), and 25.81% (16/62), respectively. Factors such as the time to CR, the time to IVF, the frequency of COS, maintenance treatment before IVF, and histology type were not found to significantly affect recurrence (p = 0.079, 0.182, 0.093, 0.267, and 0.41, respectively). Live births (hazard ratio (HR): 0.28, 95% CI: 0.082–0.962, p = 0.043) and the protocol of letrozole plus gonadotropin-releasing hormone (GnRH) antagonist/agonist used during IVF (HR: 0.1, 95% CI: 0.011–0.882, p = 0.038) were identified as independent favorable factors for recurrence.ConclusionsLive birth was associated with decreased recurrence of EEC. Reducing estrogen levels during COS may serve to mitigate the risk of endometrial cancer recurrence.

Abstract A system for itaconic acid synthesis from cellulose by Neurospora crassa was established, resulting in the highest yield of itaconic acid was 354.08 + 35.99 mg/L. Meanwhile, cellulase activity increased significantly, without any strain modifications for improved cellulase production. Multi-omics analyses showed that itaconic acid synthesis reduced energy production, leading to decreases in trehalose, cell wall, fatty acids synthesis and downregulations in MAPK signaling pathway, cell cycle and meiosis. More importantly, the low-energy environment enhanced the energy-efficient cellobionic acid/gluconic acid pathway, and the cellulase composition also changed significantly, manifested as the up-regulation of LPMOs and the down-regulation of β-glucosidases. Enhancing LPMOs-cellobionic acid/gluconic acid system has the potential to reduce energy consumption of the consolidated bioprocessing. These findings offer an overview of resource allocations by N. crassa in response to itaconic acid synthesis and highlight a series of intriguing connections between itaconic acid synthesis and cellulase synthesis in consolidated bioprocessing.

It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented.

The cement industry, as one of the primary contributors to global greenhouse gas emissions, accounts for 7% of the world’s carbon dioxide emissions. There is an urgent need to establish a rapid method for detecting cement plants to facilitate effective monitoring. In this study, a comprehensive method based on YOLOv5-IEG and the Thermal Signature Detection module using Google Earth optical imagery and SDGSAT-1 thermal infrared imagery was proposed to detect large-scale cement plant information, including geographic location and operational status. The improved algorithm demonstrated an increase of 4.8% in accuracy and a 7.7% improvement in MAP@.5:95. In a specific empirical investigation in China, we successfully detected 781 large-scale cement plants with an accuracy of 90.8%. Specifically, of the 55 cement plants in Shandong Province, we identified 46 as operational and nine as non-operational. The successful application of advanced models and remote sensing technology in efficiently and accurately tracking the operational status of cement plants provides crucial support for environmental protection and sustainable development.

The prevalent high-energy, high-pollution and high-emission economic model has led to significant air pollution challenges in recent years. The industrial sector in the Beijing–Tianjin–Hebei (BTH) region is a notable source of atmospheric pollutants, with industrial heat sources (IHSs) being primary contributors to this pollution. Effectively managing emissions from these sources is pivotal for achieving air pollution control goals in the region. A new three-stage model using multi-source long-term data was proposed to estimate atmospheric, delicate particulate matter (PM2.5) concentrations caused by IHS. In the first stage, a region-growing algorithm was used to identify the IHS radiation areas. In the second and third stages, based on a seasonal trend decomposition procedure based on Loess (STL), multiple linear regression, and U-convLSTM models, IHS-related PM2.5 concentrations caused by meteorological and anthropogenic conditions were removed using long-term data from 2012 to 2021. Finally, this study analyzed the spatial and temporal variations in IHS-related PM2.5 concentrations in the BTH region. The findings reveal that PM2.5 concentrations in IHS radiation areas were higher than in background areas, with approximately 33.16% attributable to IHS activities. A decreasing trend in IHS-related PM2.5 concentrations was observed. Seasonal and spatial analyses indicated higher concentrations in the industrially dense southern region, particularly during autumn and winter. Moreover, a case study in Handan’s She County demonstrated dynamic fluctuations in IHS-related PM2.5 concentrations, with notable reductions during periods of industrial inactivity. Our results aligned closely with previous studies and actual IHS operations, showing strong positive correlations with related industrial indices. This study’s outcomes are theoretically and practically significant for understanding and addressing the regional air quality caused by IHSs, contributing positively to regional environmental quality improvement and sustainable industrial development.

ABSTRACT: Amyloid fibrils have many excellent functional properties that facilitate their applications in the food industry. There are 2 pathways for whey protein concentrate (WPC) to form amyloid fibril aggregates: spontaneous pathway and nuclear induction pathway. Low ionic strength is a necessary condition for the spontaneous pathway to proceed successfully. In this paper, the effect of salt ions on 2 WPC fibrillation pathways was investigated by adding CaCl2. The results demonstrated WPC fibrils were unable to form normally through spontaneous pathway as adding CaCl2; but still could form through nuclear induction pathway with 20 to 30 mM CaCl2, the nuclei accelerated the fibrillation process led to the resistance to the disordered aggregation brought by CaCl2. Moreover, divalent cations (Ca2+, Mg2+) had much stronger effects than monovalent cations (Na+) on fibril formation, and the results of X-ray photoelectron spectrum together with Fourier-transform infrared spectroscopy suggested that Ca2+ had a greater effect on the fibril formation than Cl−.

The desert–oasis interaction zone plays a crucial role in safeguarding oasis ecological security and maintaining stability within oases. This paper proposed a framework of EN-DSS, based on long-term remote sensing data and fundamental data, adopted morphological spatial pattern analysis (MSPA) and Linkage Mapper among other methods, and it took Lingwu City in the Ningxia Hui Autonomous Region, which is located in the desert–oasis interaction zone in the upper reaches of the Yellow River, as a case study. The results indicate the following: since 1995, this desert–oasis system has exhibited the characteristics whereby the oasis is expanding eastward and the desert is significantly receding. The vegetation coverage has improved overall, forming an ecological security pattern characterized predominantly by shrub forests, which is referred to as the “one core, two corridors, three zones, and multiple clusters” pattern. This pattern has significantly reduced the risk of wind and sand erosion in the agricultural irrigation areas along the Yellow River. However, the construction of this ecological security pattern still faces challenges, including high construction and maintenance costs and the need to enhance the network’s quality. In the future, it will be necessary to strengthen the integrated ecological network construction of ecological areas, agricultural areas, and urban areas to enhance the stability of this regional ecological network system.

The significant heterogeneity in the spatial distribution of point of interest (POI) data, the absence of human socio-economic activity information in remote sensing images (RSI), and the high cost of land use (LU) data acquisition restrict their application in PLES spatial identification. Utilizing easily accessible data for detailed spatial identification of PLES remains an urgent challenge, especially when selecting a study area that encompasses both urban built-up areas (UBUA) and non-urban built-up areas (NUBUA). To address this issue, we proposed a PLES spatial identification method that combines POI data and land cover (LC) data in this paper. The proposed method first classified spatial analysis units (SAUs) into agricultural production space (APS), ecological space (ES), and ambiguous space (AS) based on the rich surface physical information from LC data. Subsequently, the AS was further classified into living space (LS) and non-agricultural production space (NAPS) based on the rich human socioeconomic information from POI data. For the AS that contains no POI, a simple rule was established to differentiate it into LS or NAPS. The effectiveness of the method was verified by accuracy evaluation and visual comparison. Applying the method to the Jiaodong Peninsula, we identified the PLES of the Jiaodong Peninsula for 2018 and 2022, further explored their spatial distribution characteristics, and analyzed their changes. Finally, we conducted a discussion on the real-world situations and driving mechanisms of the PLES changes and proposed several policy insights. The results indicated that both the spatial distribution characteristics of PLES and PLES change in the Jiaodong Peninsula were obvious and showed significant differentiation between UBUA and NUBUA. Climatic and natural resource conditions, geographic location, macro-policies, and governmental behaviors drove the PLES changes.

Abstract The berries of Lycium barbarum L. (Goji) are widely used as a Chinese traditional herbal medicine and functional food because of their reported beneficial pharmacological effects. However, there are reports of Goji berries being contaminated by chemical residues that could pose a hazard to humans. In this study, samples of L. barbarum L. berries were collected from plantations in a genuine production area and supermarkets in Ningxia, China. The major hazardous chemicals, including pesticides (dichlorvos, omethoate, cypermethrin, fenvalerate, malathion, and deltamethrin) and metals (lead (Pb), cadmium (Cd), copper (Cu), nickel (Ni), zinc (Zn), and arsenic (As)), were quantified by gas chromatography and inductively coupled plasma-optical emission spectrometry. In addition, associated daily exposures and health risks were determined using deterministic and probabilistic assessments. The levels of five pesticides from the plantation samples were considerably lower than the maximum residue limits; only dichlorvos was detected in the supermarket samples, and deltamethrin was not detected in any samples. Cu, Zn, As, Pb, Ni and Cd were detected in samples from both sources. The hazard quotient values of individual hazardous chemicals and the hazard index of combined hazardous chemicals were considerably less than 1, indicating the absence of a non-carcinogenic effect of hazardous chemical exposures through Goji berry consumption. The R value of As was much less than 10–6, which shows that consumption of the Goji berries had no obvious carcinogenic risks. The potentially harmful effects of the L. barbarum L. are more likely from berries obtained from plantations than those from supermarkets, and metal exposure is more dangerous than pesticide exposure. However, on the basis of our analysis, no population would be exposed hazardous chemicals exceeding existing standards, and the factors most affecting the health risk were exposure frequency and As content.

Research questionIs there an association between intrauterine hematoma (IUH), vanishing twin syndrome (VTS), and subsequent complications in twin pregnancies after in vitro fertilization (IVF)? What are the risk factors for these complications?DesignWomen who presented with two live gestational sacs following double embryo transfer were included. Patients with systematic diseases, artificial fetal reduction, and incomplete data were excluded. Further stratification of IUH pregnancies was performed according to IUH-related characteristics (i.e., volume, changing pattern, and relationship with fetal cardiac activities). The primary outcome was the incidence of VTS, while adverse outcomes in the surviving singleton and the gestational age of VTS were secondary outcomes.ResultsThe incidence of IUH was 13.8%. A total of 1,078 twin pregnancies including 539 IUH pregnancies and 539 non-IUH pregnancies were included. IUH pregnancy was associated with higher risks of VTS (26.9% vs. 18.7%, p = 0.001) as well as a higher incidence of preterm birth (p = 0.001, crude OR = 1.98, 95% CI 1.28–3.09, adjusted OR = 1.19, 95% CI 1.09–1.24), threatened abortion (p < 0.001, crude OR = 9.12, 95% CI 2.90–28.69, adjusted OR = 6.63, 95% CI 1.69–14.67), and postpartum hemorrhage (p = 0.024, crude OR = 3.13, 95% CI 1.09–8.99, adjusted OR = 1.16, 95% CI 1.08–1.32) in the surviving singleton. There was no significant difference in risks of other complications. The absence of fetal cardiac activities at the diagnosis of IUH predicted VTS (p < 0.001, crude OR 4.67, 95% CI 3.67–5.78, adjusted OR 3.33, 95% CI 1.56–5.14) and fetal loss at smaller gestational age (7.81 ± 2.10 vs. 11.39 ± 5.60 weeks, p < 0.001), while an IUH with an increasing volume did not increase the risk of VTS but might induce threatened abortion in the surviving fetus (p < 0.001, crude OR 1.84, 95% CI 1.32–2.55, adjusted OR 1.72, 95% CI 1.13–2.13).ConclusionsIUH was a risk factor for VTS in twin pregnancies following double embryo transfer and elevated the risks of threatened abortion, preterm birth, and postpartum hemorrhage in the surviving singleton. The absence of fetal cardiac activities at the diagnosis of IUH elevated the risks of VTS, while an IUH with an increasing volume was associated with threatened abortion without elevating the risks of VTS. An IUH diagnosed before the presence of fetal cardiac activities also resulted in an earlier miscarriage. The study suggests that attention be paid to twin pregnancies with first-trimester IUH to prevent VTS and subsequent adverse perinatal outcomes.HighlightsFirst-trimester intrauterine hematoma (IUH) following double embryo transfer is associated with a higher incidence of vanishing twin syndrome (VTS) and elevated subsequent risk of threatened abortion, preterm birth, and postpartum hemorrhage in the surviving singleton. Other perinatal outcomes were not associated with the diagnosis of first-trimester IUH. The absence of fetal cardiac activities at the diagnosis of IUH was of predictive value toward VTS, while an IUH with an increasing size was associated with threatened abortion without elevating the risk of VTS. Incomplete fetal cardiac activities and earlier detection of an IUH might also predict miscarriage at smaller gestational age.

Important for promoting the integrated protection and systematic management of mountains, rivers, forests, farmlands, lakes, grasslands, and sandy areas, ecological networks form the backbone of the regional ecological security pattern. An improved morphological spatial pattern analysis coupled with a minimum cumulative resistance model (MSPA–MCR) based on multi-source data was used to study, on a provincial scale, the ecological security pattern of Ningxia, an ecologically fragile region in the upper reaches of the Yellow River in China. The results show the following: (1) A reasonable classification of ecological sources and ecological corridors is key to constructing hierarchical ecological networks. Classifying ecological sources by replacing patch areas with energy factors and identifying the importance of ecological corridors by modifying the gravity model with the energy factors proposed in this paper could improve the rationality of the hierarchical structure division of ecological networks. (2) Grassland as the substrate vegetation type is an important ecological source type in arid and semi-arid ecologically fragile areas, and forests and lake wetlands are the main ecological source types in mountainous areas and oasis areas, respectively. The study area was located in the arid–semi-arid transitional area, with a variety of ecological types, such as mountain, oasis, and desert. Therefore, the complex ecological source types of forest–grassland–wetland appear in some areas. (3) There are 45 ecological patch groups that can be classified as ecological sources in Ningxia, including 10 primary source groups. The number of primary source groups is small, and their spatial distribution is unbalanced. There are two categories of ecological corridors, the river corridor and the mountain corridor, and the network connectivity is poor. (4) The ecological network structure of Ningxia is presented as an ecological security structure consisting of one belt, three screens, three corridors, and five clusters, forming a hierarchical nested ecological network security structure system.

BackgroundAdenomyosis is commonly encountered in infertile women; however, it is still unclear whether adenomyosis has a detrimental effect on in vitro fertilization and embryo transfer (IVF-ET) outcomes.MethodWe enrolled 1146 patients with adenomyosis and 1146 frequency-matched control women in a 1:1 ratio based on age, BMI, and basal follicle-stimulating hormone (FSH) level. After controlling for other factors, the rates of clinical pregnancy, miscarriage, live birth, and obstetric complications were compared between two groups.ResultsThere was no significant difference in clinical pregnancy rate between the two groups (38.1% vs. 41.6%; P=0.088). The implantation rate (25.6% versus 28.6%, P=0.027) and live birth rate (26% versus 31.5%, P=0.004) were significantly lower in the women with adenomyosis than in the controls. The miscarriage rate in the adenomyosis group was higher than that in the control group (29.1% versus 17.2%, P=0.001). After adjusting for confounding factors, multivariate analysis showed the clinical pregnancy rate was not statistically different between the two groups (OR: 0.852, P=0.070). In the adenomyosis group, the rate of miscarriage(OR: 1.877, P=0.000), placenta previa (OR: 2.996, P=0.042)and preeclampsia (OR: 2.287, P=0.042)were increased significantly, while live birth rate (OR: 0.541, P=0.000) was reduced significantly than control group.ConclusionAdenomyosis has negative effect on IVF-ET outcomes in which miscarriage risk increased, live birth rate reduced and obstetric complications increased.

Objective: To evaluate the clinical outcomes of assisted reproductive technology (ART) on fertility preservation and infertility treatment in breast cancer patients who had undergone different cancer therapies. Methods: 20 infertile females who had undergone breast cancer treatments during 2011–2018 were studied retrospectively. The patients were divided into two groups based on their cancer treatment methods and their needs of fertility preservation: the combined treatment group, who had both breast cancer surgery combining with any of the three treatments (adjuvant endocrine therapy, radiotherapy or chemotherapy), and the surgery only group. A group of infertile females without breast cancer history were used as a control group. An aromatase inhibitor Letrozole - based ovarian micro-stimulation protocol was used in females from the three groups for in vitro fertilization and embryo transfer. The ART clinical outcomes were evaluated by using the parameters of antral follicle count (AFC), the ratio of FSH/LH, oocyte retrieval number, 2 pronucleus(2 PN) fertilization rate, high-quality embryo rate, clinical pregnancy rate, and delivery outcome. Results: The surgery only group had significantly lower ratio of FSH/LH than the combined treatment group and the control group. No significant difference on the ART clinical outcomes, evaluated by the aforementioned criteria, were found between the three groups. Conclusions: Breast cancer surgery with adjuvant therapies, but not surgery alone, may damage ovarian function. The best time-limited window to preserve fertility for breast cancer patients is after surgery but before the initiation of adjuvant therapies. Importantly, the clinical outcomes of aromatase inhibitor-based ovary micro-stimulation in breast cancer patients are similar to that in non-breast cancer controls.

ObjectiveTo explore the clinical outcomes of unstimulated in vitro maturation (IVM) after oocyte retrieval with gynecological surgery (IVM-surgery) for refractory polycystic ovary syndrome (PCOS) and analyze the influencing factors.MethodsPatients with refractory PCOS who underwent unstimulated IVM-surgery from June 2014 to September 2018 were included in this retrospective cohort study. Matured IVM oocytes were freshly fertilized and subsequently frozen at the blastocyst stage. Frozen-thawed embryo transfer was then conducted according to the desire of patients. Oocytes and embryological outcomes, reproductive outcomes were evaluated. Influencing factors of oocytes and embryological outcomes were analyzed by univariate analysis and multivariate analysis. Receiver operating characteristic curves were used to evaluate the predict value of serum hormone levels for oocytes and embryological outcomes.ResultsA total of 93 patients with refractory PCOS who underwent unstimulated IVM-surgery were included in this study.13 patients (13/85, 15.3%) had spontaneous pregnancy and live birth after surgery. 34 patients (34/93, 36.6%) obtained blastocysts and received embryo transfer, of which 13 patients (13/34, 38.2%) eventually achieved live birth by IVM. Higher anti-Mullerian hormone, antral follicle count and basal serum luteinizing hormone (LH) levels were strongly correlated with higher number of oocytes retrieved (P = 0.004, 0.004, 0.040, respectively). Higher basal serum follicle-stimulating hormone (FSH) and LH were significantly associated with higher oocyte maturation rate (P = 0.001 and P = 0.004, respectively) and blastocyst formation (P = 0.036 and P = 0.003, respectively). There was a significant linear correlation between basal serum FSH and LH (r = 0.500, P

Introduction The aetiology of sleep disruptions is unknown, but hormonal fluctuations during the menstrual cycle, pregnancy and menopause have been shown to potentially affect how well a woman sleeps. The aim of this systematic review was to investigate whether hormonal contraceptives are associated with a decreased quality of sleep and increased sleep duration in women of reproductive age.Methods This review will analyse data from randomised controlled trials or non-randomised comparative studies investigating the association between hormonal contraceptives and sleep outcomes among women of reproductive age. Reviews addressing the same research question with similar eligibility criteria will be included. A literature search will be performed using the MEDLINE, Embase and Cochrane Central Register of Controlled Trials databases from inception to 7 March 2021. The Cochrane Collaboration’s Risk of Bias for Randomised Trials V.2.0 and The Risk of Bias for Non-randomised Studies of Interventions tool will be used to assess risk of bias for each outcome in eligible studies. Two reviewers will independently assess eligibility of studies and risk of bias and extract the data. All extracted data will be presented in tables and narrative form. For sleep measures investigated by two or more studies with low heterogeneity, we will conduct random-effects meta-analysis to estimate the magnitude of the overall effect of hormonal contraceptives. If studies included in this systematic review form a connected network, a network meta-analysis will be conducted to estimate the comparative effect of different contraceptives. The Grading of Recommendations, Assessment, Development, and Evaluation approach will be used to summarise the quality of evidence. Our protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols 2015 guidelines.Ethics and dissemination Ethics approval is not required as data were sourced from previously reported studies. The findings of this review will be published in a peer-reviewed journal and presented at relevant conferences.PROSPERO registration number CRD42020199958.

ObjectiveHyperlipidemia are common polycystic ovarian syndrome (PCOS)-related metabolic dysfunctions and can adversely affect assisted reproductive technology (ART) outcomes in controlled ovarian hyperstimulation (COH) cycles. The objective of this study is to analyze the relationship between lipid metabolism and ART outcomes in unstimulated natural cycles without the utilization of ovarian induction drugs, which is still uncertain.MethodsThis retrospective study included infertile women with PCOS between 21 and 40 years old undergoing unstimulated natural cycles from January 01, 2006 to December 31, 2019. Lipid metabolism was measured by body mass index (BMI) and serum biochemical parameters including total cholesterol (TC), triglycerides (TG), high and low density lipoprotein cholesterol (HDL-C and LDL-C). ART outcomes were measured by number of oocytes retrieved, oocyte maturation quality and developmental potential, clinical pregnancy and live birth.ResultsA total of 586 patients were included in this study. Multivariate Poisson log-linear analysis showed that high TC (≥5.18 mmol/L), triglycerides (TG) (≥1.76 mmol/L), LDL-C (≥3.37 mmol/L) levelsand low HDL-C levels (≤1.04 mmol/L) were significantly (PTC = 0.001, PTG < 0.001, PHDL–C < 0.001, PLDL–C < 0.001) associated with increased number of oocytes retrieved. BMI was significantly negatively associated with maturation rate (P < 0.001), fertilization rate (P < 0.001) and transferrable embryo rate (P = 0.002). High TG levels and low HDL-C levels were also associated with decreased maturation rate (PTG < 0.001, PHDL–C = 0.026). Logistic regression analysis showed statistically significant association between obesity (≥28.0 kg/m2) and decreased live birth rate (P = 0.004) as well as cumulative live birth rate (P = 0.007).ConclusionThis is the first study that focused on the relationship between basal lipid metabolism and ART outcomes in women with PCOS undergoing unstimulated natural cycles. The results showed that high levels of lipid metabolic parameters were associated with increased number of oocytes retrieved and obesity was closely associated with impaired oocyte maturation quality and developmental potential as well as poor live birth outcomes.

Anthropogenic carbon dioxide (CO2) emissions from bottom-up inventories have high uncertainties due to the usage of proxy data in creating these inventories. To evaluate bottom-up inventories, satellite observations of atmospheric CO2 with continuously improved accuracies have shown great potential. In this study, we evaluate the consistency and uncertainty of four gridded CO2 emission inventories, including CHRED, PKU, ODIAC, and EDGAR that have been commonly used to study emissions in China, using GOSAT and OCO-2 satellite observations of atmospheric column-averaged dry-air mole fraction of CO2 (XCO2). The evaluation is carried out using two data-driven approaches: (1) quantifying the correlations of the four inventories with XCO2 anomalies derived from the satellite observations; (2) comparing emission inventories with emissions predicted by a machine learning-based model that considers the nonlinearity between emissions and XCO2. The model is trained using long-term datasets of XCO2 and emission inventories from 2010 to 2019. The result shows that the inconsistencies among these four emission inventories are significant, especially in areas of high emissions associated with large XCO2 values. In particular, EDGAR shows a larger difference to CHRED over super-emitting sources in China. The differences for ODIAC and EDGAR, when compared with the machine learning-based model, are higher in Asia than those in the USA and Europe. The predicted emissions in China are generally lower than the inventories, especially in megacities. The biases depend on the magnitude of inventory emissions with strong positive correlations with emissions (R2 is larger than 0.8). On the contrary, the predicted emissions in the USA are slightly higher than the inventories and the biases tend to be random (R2 is from 0.01 to 0.5). These results indicate that the uncertainties of gridded emission inventories of ODIAC and EDGAR are higher in Asian countries than those in European and the USA. This study demonstrates that the top-down approach using satellite observations could be applied to quantify the uncertainty of emission inventories and therefore improve the accuracy in spatially and temporally attributing national/regional totals inventories.

Abstract Background Endometriosis is thought to affect the effectiveness of ART by an increased risk of miscarriage. We aimed to investigate the impact of endometriosis in women achieving singleton pregnancies through IVF fresh cycles and risk of miscarriage. Methods This retrospective cohort study included all women undergoing a first IVF cycle and achieving singleton pregnancies after fresh embryo transfer in a tertiary university hospital reproductive medical center between January 2008 and June 2016. Women with endometriosis were compared with women with no endometriosis. Women in the endometriosis group were all with a history of laparoscopy or laparotomy for endometriosis and/or with ovarian endometrioma. The control group was matched 1:2 according to age and study period. Results Among the cohort, we identified 1006 women with endometriosis as study group and 2012 unaffected women matched in a 1:2 ratios as control group. The miscarriage rate between women with and without endometriosis was similar (22.4 and 20.1%, P = 0.085). The odds ratio after adjusting for the risk factors for miscarriage was 1.14 (95% confidence interval 0.95–1.37). In the study group, the women with and without endometrioma did not show a significant risk of miscarriage, (19.8 and 23.8%, P = 0.152, OR 0.79, 95% CI 0.58–1.09). The miscarriage rate in women with endometrioma ≥30 mm (37.3 ± 7.1 mm) and

Background: Influence of pre-retrieval human chorionic gonadotropin (HCG) priming on outcomes of in vitro maturation (IVM) remains controversial. This study aimed to evaluate the effect of HCG priming before oocyte retrieval on clinical outcomes of IVM cycles in patients with polycystic ovarian syndrome (PCOS).Methods: This was a retrospective cohort study analyzing data from the first IVM cycles of unstimulated PCOS patients in a reproductive center of university affiliated hospital from January 2006 to December 2017. Patients received HCG injection before oocyte retrieval were assigned to HCG priming group and those without HCG administration were categorized as none HCG priming (Non-HCG) group. Main outcomes included oocyte maturation rate, number of embryos available, clinical pregnancy rate, and live birth rate. Candidate factors of clinical pregnancy rate was explored by univariate analysis and multivariate logistic regression analysis.Results: There were 324 patients meeting the inclusion and exclusion criteria. Among them, 129 women received HCG priming and 195 other did not. Women in HCG group had significantly lower basal FSH level (5.17 ± 1.63 vs. 5.80 ± 2.38) than Non-HCG group. Both FSH levels were

Industrial heat sources have made a great contribution to Chinese economic development. However, it has also been found that emissions from industrial heat sources are the main contribution to regional air pollution. Therefore, the detection of industrial heat sources and the expression of related information is becoming important. In this paper, the detection of industrial heat sources was used to express industrial information, thus that the accuracy of the detection of industrial thermal anomalies could be improved and the problems of noise and missing parameters addressed. A model for expressing industrial information based on object-oriented industrial heat sources and using multi-source thermal anomaly data in China was, therefore, proposed. It was a new real-time, objective, and real way to describe the production operation status of industrial heat sources on a large-scale area. First, 4340 working industrial heat sources in mainland China were detected by applying an adaptive k-means algorithm to ACF (NPP VIIRS 375-m active fire/hotspot data) data from the period 19 January 2012 to 31 December 2020. Secondly, several features of working industrial heat sources were extracted from NPP VIIRS 375-m active fire/hotspot data (ACF), VIIRS Nightfire data (VNF), and the Fires product based on Landsat-8 AIRCAS (L8F) data. Areas containing working industrial heat sources were then identified based on these different types of fire data. Light, land-surface temperature, and CO2 and N2O emissions data related to the working industrial heat sources were also extracted. The results show that feature parameters extracted from the multi-source thermal anomaly data mostly have a good positive correlation with the other parameters.

With the rapid development of remote-sensing technology and the increasing number of Earth observation satellites, the volume of image datasets is growing exponentially. The management of big Earth data is also becoming increasingly complex and difficult, with the result that it can be hard for users to access the imagery that they are interested in quickly, efficiently and intelligently. To address these challenges, this paper proposes a remote-sensing image-retrieval model based on an ensemble neural networks. This model can make full use of existing training data to improve the efficiency and accuracy of the initial retrieval of remote-sensing images and keep model simple. The retrieval of aerial images using the proposed model is compared with the results obtained using ten individual neural networks and two ensemble neural networks and the results show that the proposed approach has a high degree of precision. In addition, the coverage rate and mean precision show a dramatic improvement of more than 40% compared with existing methods based on normal way. And, the coverage ratio gets 86% for the top 10 return results.

Abstract Background Unicornuate uterus, a congenital uterine malformation resulting from unilateral maldevelopment of Mullerian duct, is more prevalent in women with infertility. Owing to relative rarity of the condition, the evidence on the associated reproductive outcomes is derived from small heterogeneous studies that report different clinical endpoints and often do not account for the anatomical variations of unicornuate uterus. The aim of this study was to evaluate the embryological and clinical outcomes following IVF-ICSI treatment in women with unicornuate uterus without rudimentary functional cavity (ESHRE-ESGE class IVb). Methods Retrospective nested case-control study comprised 342 women with unicornuate uterus and 1026 matched controls who underwent IVF-ICSI cycles between October 2012 and October 2016. Cumulative live birth rate upon one complete IVF cycle, including transfers of all resulting embryos was considered as a primary outcome measure. Results Baseline characteristics were comparable between the unicornuate uterus and control groups except for higher rate of primary infertility in unicornuate uterus. Ovarian response to stimulation did not differ between the groups. Transfer of day-3 embryos in fresh cycle resulted in lower clinical pregnancy rate (35.9% vs. 43.9%, p = 0.028) and live-birth rate (26.9% vs. 35.2%, p = 0.017) per transfer, but the difference was not observed when either cleavage frozen-thaw embryos or blastocysts were transferred. Implantation rate was lower and miscarriage rate was higher in women with unicornuate uterus but the difference between the groups did not reach statistical significance. Transfer of cleavage embryos resulted in significantly higher miscarriage rate and lower live-birth rate in fresh versus frozen-thaw cycles in each group, whereas fresh and frozen-thaw blastocyst embryos had comparable outcomes. Upon completion of one IVF-ICSI cycle, the cumulative pregnancy rate (53.1% vs. 65.7, p

Considering the complexity of landslide hazards, their manual investigation lacks efficiency and is time-consuming, especially in high-altitude plateau areas. Therefore, extracting landslide information using remote sensing technology has great advantages. In this study, comprehensive research was carried out on the landslide features of high-resolution remote sensing images on the Mangkam dataset. Based on the idea of feature-driven classification, the landslide extraction model of a fully convolutional spectral–topographic fusion network (FSTF-Net) based on a deep convolutional neural network of multi-source data fusion is proposed, which takes into account the topographic factor (slope and aspect) and the normalized difference vegetation index (NDVI) as multi-source data input by which to train the model. In this paper, a high-resolution remote sensing image classification method based on a fully convolutional network was used to extract the landslide information, thereby realizing the accurate extraction of the landslide and surrounding ground-object information. With Mangkam County in the southeast of the Qinghai–Tibet Plateau China as the study area, the proposed method was evaluated based on the high-precision digital elevation model (DEM) generated from stereoscopic images of Resources Satellite-3 and multi-source high-resolution remote sensing image data (Beijing-2, Worldview-3, and SuperView-1). Results show that our method had a landslide detection precision of 0.85 and an overall classification accuracy of 0.89. Compared with the latest DeepLab_v3+, our model increases the landslide detection precision by 5%. Thus, the proposed FSTF-Net model has high reliability and robustness.

Heavy industrial burning contributes significantly to the greenhouse gas (GHG) emissions. It is responsible for almost one-quarter of the global energy-related CO2 emissions and its share continues to grow. Mostly, those industrial emissions are accompanied by a great deal of high-temperature heat emissions from the combustion of carbon-based fuels by steel, petrochemical, or cement plants. Fortunately, these industrial heat emission sources treated as thermal anomalies can be detected by satellite-borne sensors in a quantitive way. However, most of the dominant remote sensing-based fire detection methods barely work well for heavy industrial heat source discernment. Although the object-oriented approach, especially the data clustering-based approach, has guided a novel method of detection, it is still limited by the costly computation and storage resources. Furthermore, when scaling to a national, or even global, long time-series detection, it is greatly challenged by the tremendous computation introduced by the incredible large-scale data clustering of tens of millions of high-dimensional fire data points. Therefore, we proposed an improved parallel identification method with geocoded, task-tree-based, large-scale clustering for the spatial-temporal distribution analysis of industrial heat emitters across the United States from long time-series active Visible Infrared Imaging Radiometer Suite (VIIRS) data. A recursive k-means clustering method is introduced to gradually segment and cluster industrial heat objects. Furthermore, in order to avoid the blindness caused by random cluster center initialization, the time series VIIRS hotspots data are spatially pre-grouped into GeoSOT-encoded grid tasks which are also treated as initial clustering objects. In addition, some grouped parallel clustering strategy together with geocoding-aware task tree scheduling is adopted to sufficiently exploit parallelism and performance optimization. Then, the spatial-temporal distribution pattern and its changing trend of industrial heat emitters across the United States are analyzed with the identified industrial heat sources. Eventually, the performance experiment also demonstrated the efficiency and encouraging scalability of this approach.

Class imbalance is a key issue for the application of deep learning for remote sensing image classification because a model generated by imbalanced samples training has low classification accuracy for minority classes. In this study, an accurate classification approach using the multistage sampling method and deep neural networks was proposed to classify imbalanced data. We first balance samples by multistage sampling to obtain the training sets. Then, a state-of-the-art model is adopted by combining the advantages of atrous spatial pyramid pooling (ASPP) and Encoder-Decoder for pixel-wise classification, which are two different types of fully convolutional networks (FCNs) that can obtain contextual information of multiple levels in the Encoder stage. The details and spatial dimensions of targets are restored using such information during the Decoder stage. We employ four deep learning-based classification algorithms (basic FCN, FCN-8S, ASPP, and Encoder-Decoder with ASPP of our approach) on multistage training sets (original, MUS1, and MUS2) of WorldView-3 images in southeastern Qinghai-Tibet Plateau and GF-2 images in northeastern Beijing for comparison. The experiments show that, compared with existing sets (original, MUS1, and identical) and existing method (cost weighting), the MUS2 training set of multistage sampling significantly enhance the classification performance for minority classes. Our approach shows distinct advantages for imbalanced data.

Considering the classification of high spatial resolution remote sensing imagery, this paper presents a novel classification method for such imagery using deep neural networks. Deep learning methods, such as a fully convolutional network (FCN) model, achieve state-of-the-art performance in natural image semantic segmentation when provided with large-scale datasets and respective labels. To use data efficiently in the training stage, we first pre-segment training images and their labels into small patches as supplements of training data using graph-based segmentation and the selective search method. Subsequently, FCN with atrous convolution is used to perform pixel-wise classification. In the testing stage, post-processing with fully connected conditional random fields (CRFs) is used to refine results. Extensive experiments based on the Vaihingen dataset demonstrate that our method performs better than the reference state-of-the-art networks when applied to high-resolution remote sensing imagery classification.

With the rapid development of satellite remote sensing technology, the size of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from massive image databases. In this paper, content-based image retrieval is successfully applied to change detection, and a content-based remote sensing image change information retrieval model is introduced. First, the construction of a new model framework for change information retrieval from a remote sensing database is described. Then, as the target content cannot be expressed by one kind of feature alone, a multiple-feature, integrated retrieval model is proposed. Thirdly, an experimental prototype system that was set up to demonstrate the validity and practicability of the model is described. The proposed model is a new method of acquiring change detection information from remote sensing imagery and so can reduce the need for image pre-processing and also deal with problems related to seasonal changes, as well as other problems encountered in the field of change detection. Meanwhile, the new model has important implications for improving remote sensing image management and autonomous information retrieval. The experiment results obtained using a Landsat data set show that the use of the new model can produce promising results. A coverage rate and mean average precision of 71% and 89%, respectively, were achieved for the top 20 returned pairs of images.

Remote sensing (RS) images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing buildings collapse assessment in the early time after an earthquake based on aerial remote sensing image. The procedure of RS data pre-processing and crowdsourcing data collection is presented. A probabilistic model including maximum likelihood estimation (MLE), Bayes’ theorem and expectation-maximization (EM) algorithm are applied to quantitatively estimate the individual error-rate and “ground truth” according to multiple participants’ assessment results. An experimental area of Yushu earthquake is provided to present the results contributed by participants. Following the results, some discussion is provided regarding accuracy and variation among participants. The features of buildings labeled as the same damage type are found highly consistent. This suggests that the building damage assessment contributed by crowdsourcing can be treated as reliable samples. This study shows potential for a rapid building collapse assessment through crowdsourcing and quantitatively inferring “ground truth” according to crowdsourcing data in the early time after the earthquake based on aerial remote sensing image.