Experiment of Carbonate Dissolution: Implication for High Quality Carbonate Reservoir Formation in Deep and Ultradeep Basins

As the most frontiers in petroleum geology, the study of dissolution-based rock formation in deep carbonate reservoirs provides insight into pore development mechanism of petroleum reservoir space, while predicting reservoir distribution in deep-ultradeep layers. In this study, we conducted dissolution-precipitation experiments simulating surface to deep burial environments (open and semiopen systems). The effects of temperature, pressure, and dissolved ions on carbonate dissolution-precipitation were investigated under high temperature and pressure (~200°C; ~70 Mpa) with a series of petrographic and geochemical analytical methods. The results showed that the window-shape dissolution curve appeared in 75~150°C in the open system and 120~175°C in the semiopen system. Furthermore, the dissolution weight loss of carbonate rocks in the open system was higher than that of semiopen system, making it more favorable for gaining porosity. The type of fluid and rock largely determines the reservoir quality. In the open system, the dissolution weight loss of calcite was higher than that of dolomite with 0.3% CO2 as the reaction fluid. In the semiopen system, the weight loss from dolomitic limestone prevailed with 0.3% CO2 as the reaction fluid. Our study could provide theoretical basis for the prediction of high quality carbonate reservoirs in deep and ultradeep layers

Epidemiological characteristics of Vibrio parahaemolyticus outbreaks, Zhejiang, China, 2010–2022

BackgroundVibrio parahaemolyticus is one of the most common foodborne pathogens and poses a significant disease burden. The purpose of the study was to elucidate the epidemiological characteristics of Vibrio parahaemolyticus outbreaks in Zhejiang Province, and provide insights for the targeted prevention and control of foodborne diseases.MethodsDescriptive statistical methods were utilized to analyze the data on Vibrio parahaemolyticus outbreaks reported by all Centers for Disease Control and Prevention (CDCs) through Foodborne Disease Outbreaks Surveillance System (FDOSS) in Zhejiang Province from 2010 to 2022.ResultsFrom 2010 to 2022, a total of 383 outbreaks caused by Vibrio parahaemolyticus were reported by 90 CDCs in 11 prefectures of Zhejiang Province, resulting in 4,382 illnesses, 326 hospitalizations and 1 death. The main symptoms of the outbreak-related cases were diarrhea (95.18%), abdominal pain (89.23%), nausea (55.64%), vomiting (50.57%), fever (24.21%), etc. The outbreaks occurring between July and September accounted for 77.54% of all outbreaks (297 out of 383). Outbreaks associated with restaurants accounted for the majority (57.96%, 222/383) of all outbreaks, followed by those linked to staff canteens (15.40%, 59/383) and rural banquets (11.23%, 43/383). 31.85% of all outbreaks were associated with the consumption of aquatic products, while ready-to-eat foods such as Chinese cold dishes and cooked meat products accounted for 12.53% of all outbreaks. Serotype O3:K6 (81.94%, 118/144) was the predominant serotype responsible for outbreaks from 2010 to 2020, while serotype O10:K4 (57.89%, 33/57) was the predominant serotype from 2021 to 2022.ConclusionIn-depth and comprehensive analysis of long-term surveillance data on Vibrio parahaemolyticus outbreaks is essential to gain insight into the epidemiological characteristics, identify long-term patterns and recent trends, and enable governments to prioritize interventions and develop targeted policies to mitigate such outbreaks

Random resistive memory-based deep extreme point learning machine for unified visual processing

Visual sensors, including 3D LiDAR, neuromorphic DVS sensors, and conventional frame cameras, are increasingly integrated into edge-side intelligent machines. Realizing intensive multi-sensory data analysis directly on edge intelligent machines is crucial for numerous emerging edge applications, such as augmented and virtual reality and unmanned aerial vehicles, which necessitates unified data representation, unprecedented hardware energy efficiency and rapid model training. However, multi-sensory data are intrinsically heterogeneous, causing significant complexity in the system development for edge-side intelligent machines. In addition, the performance of conventional digital hardware is limited by the physically separated processing and memory units, known as the von Neumann bottleneck, and the physical limit of transistor scaling, which contributes to the slowdown of Moore's law. These limitations are further intensified by the tedious training of models with ever-increasing sizes. We propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM), that offers efficient unified point set analysis. We show the system's versatility across various data modalities and two different learning tasks. Compared to a conventional digital hardware-based system, our co-design system achieves huge energy efficiency improvements and training cost reduction when compared to conventional systems. Our random resistive memory-based deep extreme point learning machine may pave the way for energy-efficient and training-friendly edge AI across various data modalities and tasks

Expression of Activated PIK3CA in Ovarian Surface Epithelium Results in Hyperplasia but Not Tumor Formation

activation is one of the early genetic events in ovarian cancer. However, its role in malignant transformation of ovarian surface epithelium (OSE) is largely unclear..

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference On Medical Image Computing Computer Assisted Intervention (MICCAI) 2017. Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. We found that not a single algorithm performed best for liver and tumors. The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.Comment: conferenc

Global Carbon Budget 2023

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land-use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based f CO2 products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, and Earth system models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2022, EFOS increased by 0.9 % relative to 2021, with fossil emissions at 9.9 ± 0.5 Gt C yr−1 (10.2 ± 0.5 Gt C yr−1 when the cement carbonation sink is not included), and ELUC was 1.2 ± 0.7 Gt C yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 11.1 ± 0.8 Gt C yr−1 (40.7±3.2 Gt CO2 yr−1). Also, for 2022, GATM was 4.6±0.2 Gt C yr−1 (2.18±0.1 ppm yr−1; ppm denotes parts per million), SOCEAN was 2.8 ± 0.4 Gt C yr−1, and SLAND was 3.8 ± 0.8 Gt C yr−1, with a BIM of −0.1 Gt C yr−1 (i.e. total estimated sources marginally too low or sinks marginally too high). The global atmospheric CO2 concentration averaged over 2022 reached 417.1 ± 0.1 ppm. Preliminary data for 2023 suggest an increase in EFOS relative to 2022 of +1.1 % (0.0 % to 2.1 %) globally and atmospheric CO2 concentration reaching 419.3 ppm, 51 % above the pre-industrial level (around 278 ppm in 1750). Overall, the mean of and trend in the components of the global carbon budget are consistently estimated over the period 1959–2022, with a near-zero overall budget imbalance, although discrepancies of up to around 1 Gt C yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows the following: (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living-data update documents changes in methods and data sets applied to this most recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work are available at https://doi.org/10.18160/GCP-2023 (Friedlingstein et al., 2023)

m6A methyltransferase KIAA1429 accelerates oral squamous cell carcinoma via regulating glycolysis and ferroptosis

N6-methyladenosine (m6A) modification acts as the most prevalent modification on eukaryotic RNA, and its function on oral squamous cell carcinoma (OSCC) is still unclear. Here, the present research aimed to explore the novel function of m6A methyltransferase KIAA1429 in OSCC. Results illustrated that KIAA1429 up-regulated in the OSCC samples and cells. Gain/loss functional assays demonstrated that KIAA1429 repressed the ferroptosis of OSCC. Moreover, KIAA1429 positively accelerated the aerobic glycolysis of OSCC, including glucose uptake, lactate production, ATP level and ECAR. Mechanistically, KIAA1429 could install the m6A modification on the PGK1 mRNA, thereby up-regulating the methylated m6A level. Moreover, m6A reader YTHDF1 recognized the m6A modification site of PGK1 mRNA and enhanced its mRNA stability. Thus, KIAA1429 promoted the OSCC aerobic glycolysis and inhibited the ferroptosis of OSCC through YTHDF1-mediated PGK1 mRNA stability. Taken together, these findings reveal a novel insight for KIAA1429 on OSCC via m6A-dependent manner

An Evaluation and Promotion Strategy of Green Land Use Benefits in China: A Case Study of the Beijing–Tianjin–Hebei Region

Green development is the inevitable choice for global sustainable development, and China has chosen green development as its national strategy. Land use changes will affect a soil’s organic matter by changing the land’s productivity, soil quality and fertility. It is of great significance for ensuring soil fertility, improving the environment and promoting the carbon cycle that the concept of green development is implemented in the process of land use activity. Establishing an indicator system and evaluation method for a green land use benefit evaluation suitable for green development is helpful for strengthening the responsibility and consciousness of such land use, and to provide theoretical guidance and decision-making references for promoting such developments and evaluations. In this study, based on a connotation analysis of green land use, the entropy weight method and BP (Back Propagation) neural network model method were used to construct an evaluation index system for green land use benefits, including four criterion layers and eighteen evaluation indexes, and the entropy-BP neural network evaluation method was proposed to reveal the problems in green land use benefits in the Beijing–Tianjin–Hebei region. The results showed that the green land use benefit level in the region was low, while the spatial pattern was high in the north, low in the middle and high in the south. Langfang, Beijing and Handan were the lowest centers of green land ecological benefit, while Beijing and Tianjin were the lowest centers of green land economic benefit. The green governance benefit and green space benefit were in a relative spatial equilibrium. The cultivated land area, forestry products, sewage centralized treatment degree and built-up area ratio were the most important influences on the green ecological benefit, green economic benefit, green governance benefit and green space benefit, respectively. The entropy-BP neural network evaluation system and method have certain applications in the design of relevant assessment reward-and-punishment systems. Accelerating the optimization of the Beijing–Tianjin–Hebei territorial space’s development and utilization pattern, and constructing a green benefit sharing mechanism of land use, are important strategies to improve the benefits of green land use

An Evaluation and Promotion Strategy of Green Land Use Benefits in China: A Case Study of the Beijing–Tianjin–Hebei Region

Green development is the inevitable choice for global sustainable development, and China has chosen green development as its national strategy. Land use changes will affect a soil’s organic matter by changing the land’s productivity, soil quality and fertility. It is of great significance for ensuring soil fertility, improving the environment and promoting the carbon cycle that the concept of green development is implemented in the process of land use activity. Establishing an indicator system and evaluation method for a green land use benefit evaluation suitable for green development is helpful for strengthening the responsibility and consciousness of such land use, and to provide theoretical guidance and decision-making references for promoting such developments and evaluations. In this study, based on a connotation analysis of green land use, the entropy weight method and BP (Back Propagation) neural network model method were used to construct an evaluation index system for green land use benefits, including four criterion layers and eighteen evaluation indexes, and the entropy-BP neural network evaluation method was proposed to reveal the problems in green land use benefits in the Beijing–Tianjin–Hebei region. The results showed that the green land use benefit level in the region was low, while the spatial pattern was high in the north, low in the middle and high in the south. Langfang, Beijing and Handan were the lowest centers of green land ecological benefit, while Beijing and Tianjin were the lowest centers of green land economic benefit. The green governance benefit and green space benefit were in a relative spatial equilibrium. The cultivated land area, forestry products, sewage centralized treatment degree and built-up area ratio were the most important influences on the green ecological benefit, green economic benefit, green governance benefit and green space benefit, respectively. The entropy-BP neural network evaluation system and method have certain applications in the design of relevant assessment reward-and-punishment systems. Accelerating the optimization of the Beijing–Tianjin–Hebei territorial space’s development and utilization pattern, and constructing a green benefit sharing mechanism of land use, are important strategies to improve the benefits of green land use