Global and local carbon footprints of city of Hong Kong and Macao from 2000 to 2015

Hong Kong and Macao are featured with their urban metabolism as they heavily rely on the energy and resource supply from other regions. However, a comprehensive perspective is lacked to depict their CO2 emissions due to the independence of statistical data. Here we analyze the carbon footprints of Hong Kong and Macao. The direct energy-related emissions (Scope 1), the emissions of cross-boundary electricity (Scope 2), and the embodied emissions associated with trade (Scope 3) are examined. Scope 1 carbon footprints of the two areas were stabilized at 50 Mt, accounting for 0.6% of those from Mainland China in 2018. Their global footprints were approximately three times of their Scope 1 emissions, accompanied by a continuous growth between 2000 and 2015, and the contribution of their local footprints has doubled on average. Their Scope 3 emissions were mainly due to the enormous unfavorable balance of trade. Meanwhile, the increasing impact of imports' higher emission intensity on their Scope 3 emissions should not be ignored. We suggest that Hong Kong and Macao should adjust their mitigation policies that focus only on Scope 1 emissions as developed cities outsourcing production through supply chains

The Clinical and Theranostic Values of Activated Leukocyte Cell Adhesion Molecule (ALCAM)/CD166 in Human Solid Cancers

Activated leukocyte cell adhesion molecule (ALCAM), also known as CD166, is a cell adhesion protein that is found in multiple cell types. ALCAM has multiple and diverse roles in various physiological and pathological conditions, including inflammation and cancer. There has been compelling evidence of ALCAM’s prognostic value in solid cancers, indicating that it is a potential therapeutic target. The present article overviews the recent findings and progress in ALCAM and its involvement in cancer, with a primary focus on its clinical connections in cancer and therapeutic values

Estimates of daily ground-level NO2 concentrations in China based on big data and machine learning approaches

Nitrogen dioxide (NO2) is one of the most important atmospheric pollutants. However, current ground-level NO2 concentration data are lack of either high-resolution coverage or full coverage national wide, due to the poor quality of source data and the computing power of the models. To our knowledge, this study is the first to estimate the ground-level NO2 concentration in China with national coverage as well as relatively high spatiotemporal resolution (0.25 degree; daily intervals) over the newest past 6 years (2013-2018). We advanced a Random Forest model integrated K-means (RF-K) for the estimates with multi-source parameters. Besides meteorological parameters, satellite retrievals parameters, we also, for the first time, introduce socio-economic parameters to assess the impact by human activities. The results show that: (1) the RF-K model we developed shows better prediction performance than other models, with cross-validation R2 = 0.64 (MAPE = 34.78%). (2) The annual average concentration of NO2 in China showed a weak increasing trend . While in the economic zones such as Beijing-Tianjin-Hebei region, Yangtze River Delta, and Pearl River Delta, the NO2 concentration there even decreased or remained unchanged, especially in spring. Our dataset has verified that pollutant controlling targets have been achieved in these areas. With mapping daily nationwide ground-level NO2 concentrations, this study provides timely data with high quality for air quality management for China. We provide a universal model framework to quickly generate a timely national atmospheric pollutants concentration map with a high spatial-temporal resolution, based on improved machine learning methods

Phytophthora sojae Avirulence Effector Avr3b is a Secreted NADH and ADP-ribose Pyrophosphorylase that Modulates Plant Immunity

Plants have evolved pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) and effector-triggered immunity (ETI) to protect themselves from infection by diverse pathogens. Avirulence (Avr) effectors that trigger plant ETI as a result of recognition by plant resistance (R) gene products have been identified in many plant pathogenic oomycetes and fungi. However, the virulence functions of oomycete and fungal Avr effectors remain largely unknown. Here, we combined bioinformatics and genetics to identify Avr3b, a new Avr gene from Phytophthora sojae, an oomycete pathogen that causes soybean root rot. Avr3b encodes a secreted protein with the RXLR host-targeting motif and C-terminal W and Nudix hydrolase motifs. Some isolates of P. sojae evade perception by the soybean R gene Rps3b through sequence mutation in Avr3b and lowered transcript accumulation. Transient expression of Avr3b in Nicotiana benthamiana increased susceptibility to P. capsici and P. parasitica, with significantly reduced accumulation of reactive oxygen species (ROS) around invasion sites. Biochemical assays confirmed that Avr3b is an ADP-ribose/NADH pyrophosphorylase, as predicted from the Nudix motif. Deletion of the Nudix motif of Avr3b abolished enzyme activity. Mutation of key residues in Nudix motif significantly impaired Avr3b virulence function but not the avirulence activity. Some Nudix hydrolases act as negative regulators of plant immunity, and thus Avr3b might be delivered into host cells as a Nudix hydrolase to impair host immunity. Avr3b homologues are present in several sequenced Phytophthora genomes, suggesting that Phytophthora pathogens might share similar strategies to suppress plant immunity

Expression of Claudin-9 (CLDN9) in breast cancer, the clinical significance in connection with its subcoat anchorage proteins ZO-1 and ZO-3 and impact on drug resistance

(1) Introduction: Claudin-9 (CLDN9) is a member of the claudin protein family, a critical transmembrane protein family for tight junctions that are implemented in the progression of numerous cancer types. The present study investigated the role that CLDN9, along with the subcoat proteins, Zonula Occludens (ZOs), plays in clinical breast cancer and subsequent impact on drug response of patients. (2) Methods: CLDN9 protein and CLDN9 transcript were determined and correlated with clinical and pathological indicators, together with the status of hormonal receptors. The levels of CLDN9 transcript were also assessed against the therapeutic responses of the patients to chemotherapies by using a dataset from the TCGA database. Breast cancer cell models, representing different molecular subtypes of breast cancer, with differential expression of CLDN9 were created and used to assess the biological impact and response to chemotherapeutic drugs. (3) Results: Breast cancer tissues expressed significantly higher levels of the CLDN9, with the high levels being associated with shorter survival. CLDN9 was significantly correlated with its anchorage proteins ZO-1 and ZO-3. Integrated expression of CLDN9, ZO-1 and ZO-3 formed a signature that was significantly linked to overall survival (OS) (p = 0.013) and relapse-free survival (RFS) (p = 0.024) in an independent matter. CLDN9 transcript was significantly higher in patients who were resistant to chemotherapies (p < 0.000001). CLDN9 connection to chemoresistance was particularly prominent in patients of ER-positive (ER(+)), Her-2-negative((Her-2(−)), ER(+)/Her-2(−) and triple-negative breast cancers (TNBCs), but not in patients with HER-2-positive tumors. In Her-2-negative MCF7 and MDA-MB-231 cancer cells, loss of CLDN9 significantly increased sensitivity to several chemotherapeutic drugs including paclitaxel, gemcitabine and methotrexate, which was not seen in Her-2(+) SKBR3 cells. However, suppressing Her-2 using neratinib, a permanent Her-2 inhibitor, sensitized cellular response to these chemodrugs in cells with CLDN9 knockdown. (4) Conclusions: CLDN9 is an important prognostic indicator for patients with breast cancer and also a pivotal factor in assessing patient responses to chemotherapies. Her-2 is a negating factor for the treatment response prediction value by CLDN9 and negating Her-2 and CLDN9 may enhance breast cancer cellular response to chemotherapeutic drugs

Carbon Monitor Cities, near-real-time daily estimates of CO2 emissions from 1500 cities worldwide

Building on near-real-time and spatially explicit estimates of daily carbon dioxide (CO2) emissions, here we present and analyze a new city-level dataset of fossil fuel and cement emissions. Carbon Monitor Cities provides daily, city-level estimates of emissions from January 2019 through December 2021 for 1500 cities in 46 countries, and disaggregates five sectors: power generation, residential (buildings), industry, ground transportation, and aviation. The goal of this dataset is to improve the timeliness and temporal resolution of city-level emission inventories and includes estimates for both functional urban areas and city administrative areas that are consistent with global and regional totals. Comparisons with other datasets (i.e. CEADs, MEIC, Vulcan, and CDP) were performed, and we estimate the overall uncertainty to be 21.7%. Carbon Monitor Cities is a near-real-time, city-level emission dataset that includes cities around the world, including the first estimates for many cities in low-income countries