Natural gas supply from Russia derived from daily pipeline flow data and potential solutions for filling a shortage of Russian supply in the European Union (EU)

Russia is the largest natural gas supplier to the European Union (EU). The invasion of Ukraine was followed by a cutoff of gas supplies from Russia to many EU countries, and the EU is planning to ban or drastically reduce its dependence on Russia. We provide a dataset of daily gas consumption in five sectors (household and public building heating, power, industry, and other sectors) with supply source shares in the EU27 (27 EU member countries) and UK from 2016 to 2022. The datasets are available at Zenodo platform: https://doi.org/10.5281/zenodo.7549233 (Zhou et al., 2022). The dataset separates the contributions of Russian imports, liquefied natural gas (LNG) imports, and other supply sources to both direct supply and storage supply for gas consumption estimations. The dataset was developed with a gas network flow simulation model based on mass flow balance by combining data from multiple datasets including daily ENTSOG (European Network of Transmission System Operators for Gas) pipeline gas transport and storage, ENTSOE (European Network of Transmission System Operators for Electricity) daily power production from gas, and Eurostat monthly gas consumption statistics per sector. The annual consumption data were validated against the BP Statistical Review of World Energy and Eurostat datasets. We secondly analyzed the share of gas supplied by Russia in each country to quantify the “gap” that would result from a cessation of all Russian exports to Europe. Thirdly, we collected multiple data sources to assess how national gaps could be alleviated by (1) reducing the demand for heating in a plausible way using the lower envelope of gas empirical consumption – temperature functions, (2) increasing power generation from sources other than gas, (3) transferring gas savings from countries with surplus to those with deficits, and (4) increasing imports from other countries like Norway, the USA, Australia, and northern African countries from either pipelines or LNG imports, accounting for existing capacities. Our results indicate that it should be theoretically possible for the EU to collectively make up for a sudden shortfall of Russian gas by combining the four solutions together, provided a perfect collaboration between EU countries and the UK to redistribute gas from countries with surplus to those with deficits. Further analyses are required to investigate the implications with respect to the costs, including social, economic, and institutional dimensions; political barriers; and negative impacts on climate policies, with inevitable increases in CO2 emissions if the use of coal is ramped up in the power sector.</p

Evidence supported by Mendelian randomization: impact on inflammatory factors in knee osteoarthritis

BackgroundPrior investigations have indicated associations between Knee Osteoarthritis (KOA) and certain inflammatory cytokines, such as the interleukin series and tumor necrosis factor-alpha (TNFα). To further elaborate on these findings, our investigation utilizes Mendelian randomization to explore the causal relationships between KOA and 91 inflammatory cytokines.MethodsThis two-sample Mendelian randomization utilized genetic variations associated with KOA from a large, publicly accessible Genome-Wide Association Study (GWAS), comprising 2,227 cases and 454,121 controls of European descent. The genetic data for inflammatory cytokines were obtained from a GWAS summary involving 14,824 individuals of European ancestry. Causal relationships between exposures and outcomes were primarily investigated using the inverse variance weighted method. To enhance the robustness of the research results, other methods were combined to assist, such as weighted median, weighted model and so on. Multiple sensitivity analysis, including MR-Egger, MR-PRESSO and leave one out, was also carried out. These different analytical methods are used to enhance the validity and reliability of the final results.ResultsThe results of Mendelian randomization indicated that Adenosine Deaminase (ADA), Fibroblast Growth Factor 5(FGF5), and Hepatocyte growth factor (HFG) proteins are protective factors for KOA (IVWADA: OR = 0.862, 95% CI: 0.771–0.963, p = 0.008; IVWFGF5: OR = 0.850, 95% CI: 0.764–0.946, p = 0.003; IVWHFG: OR = 0.798, 95% CI: 0.642–0.991, p = 0.042), while Tumor necrosis factor (TNFα), Colony-stimulating factor 1(CSF1), and Tumor necrosis factor ligand superfamily member 12(TWEAK) proteins are risk factors for KOA. (IVWTNFα: OR = 1.319, 95% CI: 1.067–1.631, p = 0.011; IVWCSF1: OR = 1.389, 95% CI: 1.125–1.714, p = 0.002; IVWTWEAK: OR = 1.206, 95% CI: 1.016–1.431, p = 0.032).ConclusionThe six proteins identified in this study demonstrate a close association with the onset of KOA, offering valuable insights for future therapeutic interventions. These findings contribute to the growing understanding of KOA at the microscopic protein level, paving the way for potential targeted therapeutic approaches

Boosting Out-of-distribution Detection with Typical Features

Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios. Different from most previous OOD detection methods that focus on designing OOD scores or introducing diverse outlier examples to retrain the model, we delve into the obstacle factors in OOD detection from the perspective of typicality and regard the feature's high-probability region of the deep model as the feature's typical set. We propose to rectify the feature into its typical set and calculate the OOD score with the typical features to achieve reliable uncertainty estimation. The feature rectification can be conducted as a {plug-and-play} module with various OOD scores. We evaluate the superiority of our method on both the commonly used benchmark (CIFAR) and the more challenging high-resolution benchmark with large label space (ImageNet). Notably, our approach outperforms state-of-the-art methods by up to 5.11$\%$ in the average FPR95 on the ImageNet benchmark

Targeting Anion Exchange of Osteoclast, a New Strategy for Preventing Wear Particles Induced- Osteolysis

Joint replacement is essential for the treatment of serious joint disease. However, prosthetic failure remains an important clinical issue, with periprosthesis osteolysis (PO), caused by osteoclastic bone resorption induced by wear particles, being the leading cause of failure. Nuclear factor of activated T cells c1 (NFATc1) appears to play an important role in wear particle-induced osteoclastogenesis, with bicarbonate/chloride exchanger, solute carrier family 4, anion exchanger, member 2, (SLC4A2) being upregulated during osteoclastogenesis in an NFATc1-dependent manner. Anion exchange mediated by SLC4A2 in osteoclasts could affect the bone resorption activity by regulating pHi. This study investigated the role and mechanism of SLC4A2 in wear particle-induced osteoclast differentiation and function in vitro. The use of 4, 4′-diisothiocyano-2,2′-stilbenedisulfonic acid (DIDS), an anion exchange inhibitor, suppressed wear particle-induced PO in vivo. Furthermore, controlled release of DIDS from chitosan microspheres can strengthen the PO therapy effect. Therefore, anion exchange mediated by osteoclastic SLC4A2 may be a potential therapeutic target for the treatment of aseptic loosening of artificial joints

Approach to select optimal cross-correlation parameters for light field particle image velocimetry

The light field particle image velocimetry (LF-PIV) has shown a great potential for three-dimensional (3D) flow measurement in space-constrained applications. Usually, the parameters of the cross-correlation calculation in the LF-PIV are chosen based on empirical analysis or introduced from conventional planar PIV, which lowers the accuracy of 3D velocity field measurement. This study presents an approach to selecting optimal parameters of the cross-correlation calculation and thereby offers systematic guidelines for experiments. The selection criterion of the interrogation volume size is studied based on the analysis of the valid detection probability of the correlation peak. The optimal seeding concentration and the size of tracer particles are then explored through synthetic Gaussian vortex field reconstruction. The optimized parameters are employed in a cylinder wake flow measurement in a confined channel. A comparative study is conducted between the LF-PIV and a planar PIV system. Results indicate that the LF-PIV along with the optimized parameters can measure the 3D flow velocity of the cylinder wakes accurately. It has been observed that the mean and max errors of velocity decrease by 32.6% and 18.8%, respectively compared to the related LF-PIV techniques without consideration of optimal parameters. Therefore, it is suggested that the optimized cross-correlation parameters in the LF-PIV can improve the accuracy of 3D flow measurement

Spatial resolution of light field sectioning pyrometry for flame temperature measurement

The light field sectioning pyrometry (LFSP) has proven a significant advancement for in-situ measurement of flame temperature through a single light field camera. However, the spatial resolution of LFSP is limited, which severely inhibits the measurement accuracy. This paper aims to evaluate the spatial resolution of LFSP for flame temperature measurement quantitatively. A theoretical model of the spatial resolution is established based on optical parameters and point spread function of the light field camera. The spatial resolution is then numerically analyzed with different parameters of light field cameras. Based on the theoretical model, a novel cage-typed light field camera with a higher spatial resolution of LFSP is developed and experimentally evaluated. A significant improvement of spatial resolution about 17% and 50% in lateral and depth directions, respectively, is achieved. Results show that the spatial resolution is in good agreement with the theoretical model. The LFSP is then evaluated under different combustion cases and their temperatures are reconstructed

Intraperitoneal pyrophosphate treatment reduces renal calcifications in Npt2a null mice

Mutations in the proximal tubular sodium-dependent phosphate co-transporters NPT2a and NPT2c have been reported in patients with renal stone disease and nephrocalcinosis, however the relative contribution of genotype, dietary calcium and phosphate, and modifiers of mineralization such as pyrophosphate (PPi) to the formation of renal mineral deposits is unclear. In the present study, we used Npt2a-/- mice to model the renal calcifications observed in these disorders. We observed elevated urinary excretion of PPi in Npt2a-/- mice when compared to WT mice. Presence of two hypomorphic Extracellular nucleotide pyrophosphatase phosphodiesterase 1 (Enpp1asj/asj) alleles decreased urine PPi and worsened renal calcifications in Npt2a-/- mice. These studies suggest that PPi is a thus far unrecognized factor protecting Npt2a-/- mice from the development of renal mineral deposits. Consistent with this conclusion, we next showed that renal calcifications in these mice can be reduced by intraperitoneal administration of sodium pyrophosphate. If confirmed in humans, urine PPi could therefore be of interest for developing new strategies to prevent the nephrocalcinosis and nephrolithiasis seen in phosphaturic disorders

QQS orphan gene regulates carbon and nitrogen partitioning across species via NF-YC interactions

The allocation of carbon and nitrogen resources to the synthesis of plant proteins, carbohydrates, and lipids is complex and under the control of many genes; much remains to be understood about this process. QQS (Qua-Quine Starch; At3g30720), an orphan gene unique to Arabidopsis thaliana, regulates metabolic processes affecting carbon and nitrogen partitioning among proteins and carbohydrates, modulating leaf and seed composition in Arabidopsis and soybean. Here the universality of QQS function in modulating carbon and nitrogen allocation is exemplified by a series of transgenic experiments. We show that ectopic expression of QQS increases soybean protein independent of the genetic background and original protein content of the cultivar. Furthermore, transgenic QQS expression increases the protein content of maize, a C4 species (a species that uses 4-carbon photosynthesis), and rice, a protein-poor agronomic crop, both highly divergent from Arabidopsis. We determine that QQS protein binds to the transcriptional regulator AtNF-YC4 (Arabidopsis nuclear factor Y, subunit C4). Overexpression of AtNF-YC4 in Arabidopsis mimics the QQS-overexpression phenotype, increasing protein and decreasing starch levels. NF-YC, a component of the NF-Y complex, is conserved across eukaryotes. The NF-YC4 homologs of soybean, rice, and maize also bind to QQS, which provides an explanation of how QQS can act in species where it does not occur endogenously. These findings are, to our knowledge, the first insight into the mechanism of action of QQS in modulating carbon and nitrogen allocation across species. They have major implications for the emergence and function of orphan genes, and identify a nontransgenic strategy for modulating protein levels in crop species, a trait of great agronomic significance

HCV induces transforming growth factor β1 through activation of endoplasmic reticulum stress and the unfolded protein response

HCV replication disrupts normal endoplasmic reticulum (ER) function and activates a signaling network called the unfolded protein response (UPR). UPR is directed by three ER transmembrane proteins including ATF6, IRE1, and PERK. HCV increases TGF-β1 and oxidative stress, which play important roles in liver fibrogenesis. HCV has been shown to induce TGF-β1 through the generation of reactive oxygen species (ROS) and p38 MAPK, JNK, ERK1/2, and NFκB-dependent pathways. However, the relationship between HCV-induced ER stress and UPR activation with TGF-β1 production has not been fully characterized. In this study, we found that ROS and JNK inhibitors block HCV up-regulation of ER stress and UPR activation. ROS, JNK and IRE1 inhibitors blocked HCV-activated NFκB and TGF-β1 expression. ROS, ER stress, NFκB, and TGF-β1 signaling were blocked by JNK specific siRNA. Knockdown IRE1 inhibited JFH1-activated NFκB and TGF-β1 activity. Knockdown of JNK and IRE1 blunted JFH1 HCV up-regulation of NFκB and TGF-β1 activation. We conclude that HCV activates NFκB and TGF-β1 through ROS production and induction of JNK and the IRE1 pathway. HCV infection induces ER stress and the UPR in a JNK-dependent manner. ER stress and UPR activation partially contribute to HCV-induced NF-κB activation and enhancement of TGF-β1