Abundance, Stable Isotopic Composition, and Export Fluxes of DOC, POC, and DIC From the Lower Mississippi River During 2006-2008

Sources, abundance, isotopic compositions, and export fluxes of dissolved inorganic carbon (DIC), dissolved and colloidal organic carbon (DOC and COC), and particulate organic carbon (POC), and their response to hydrologic regimes were examined through monthly sampling from the Lower Mississippi River during 2006–2008. DIC was the most abundant carbon species, followed by POC and DOC. Concentration and δ13C of DIC decreased with increasing river discharge, while those of DOC remained fairly stable. COC comprised 61 ± 3% of the bulk DOC with similar δ13C abundances but higher percentages of hydrophobic organic acids than DOC, suggesting its aromatic and diagenetically younger status. POC showed peak concentrations during medium flooding events and at the rising limb of large flooding events. While δ13C-POC increased, δ15N of particulate nitrogen decreased with increasing discharge. Overall, the differences in δ13C between DOC or DIC and POC show an inverse correlation with river discharge. The higher input of soil organic matter and respired CO2 during wet seasons was likely the main driver for the convergence of δ13C between DIC and DOC or POC, whereas enhanced in situ primary production and respiration during dry seasons might be responsible for their isotopic divergence. Carbon export fluxes from the Mississippi River were estimated to be 13.6 Tg C yr−1 for DIC, 1.88 Tg C yr−1 for DOC, and 2.30 Tg C yr−1 for POC during 2006–2008. The discharge-normalized DIC yield decreased during wet seasons, while those of POC and DOC increased and remained constant, respectively, implying variable responses in carbon export to the increasing discharge

Seasonal Variations In Nutrient Concentrations and Speciation in the Chena River, Alaska

To better understand the seasonal controls on nutrient abundances, speciation, and fluxes in a watershed underlain by discontinuous permafrost, we collected water samples biweekly from the Chena River during 2005-2006 to measure inorganic and organic N, P, and Si in dissolved and particulate phases. Nitrate concentrations were low (8-14 mu M) during the winter and summer dry seasons but were elevated during the spring freshet (15-24 mu M). Ammonium varied from 8 to 13 mu M during the winter but dropped dramatically during the ice-open season to 0.1-3 mM. Phosphate was very low throughout the year (ranging from 0.03 to 0.3 mu M), reflecting the pristine condition of the watershed. Dissolved silica was high in the winter and reached its minimum during the spring freshet. DIN was the dominant species in the total N pool (60%), followed by DON (30%) and PN (10%). Most of the phosphorous was present in the particulate phase (74%), with phosphate and DOP only comprising 19% and 7%, respectively. Seasonal variations in nutrient concentrations and speciation were mostly controlled by the hydrological flow regime and biological activity in the river. Annual nutrient export fluxes from the Chena River during 2005-2006 were 51.1 x 10(6) mole-N, 1.4 x 10(6) mole-P, and 197 x 10(6) mole-Si, corresponding to an annual yield of 9.8 x 10(3) mol-N km(-2), 0.28 x 10(3) mol-P km(-2), and 37.9 x 10(3) mol-Si km(-2), respectively. Within the annual export fluxes, the spring freshet contributed about 18% of TN, 27% of TP, and 10% of Si, while the winter season contributed 11% of TN, 12% of TP, and 20% of Si. Continued climatic warming in northern watersheds will likely increase the export of nutrient species from watersheds

Cost and thermodynamic analysis of wind-hydrogen production via multi-energy systems

With rising temperatures, extreme weather events, and environmental challenges, there is a strong push towards decarbonization and an emphasis on renewable energy, with wind energy emerging as a key player. The concept of multi-energy systems offers an innovative approach to decarbonization, with the potential to produce hydrogen as one of the output streams, creating another avenue for clean energy production. Hydrogen has significant potential for decarbonizing multiple sectors across buildings, transport, and industries. This paper explores the integration of wind energy and hydrogen production, particularly in areas where clean energy solutions are crucial, such as impoverished villages in Africa. It models three systems: distinct configurations of micro-multi-energy systems that generate electricity, space cooling, hot water, and hydrogen using the thermodynamics and cost approach. System 1 combines a wind turbine, a hydrogen-producing electrolyzer, and a heat pump for cooling and hot water. System 2 integrates this with a biomass-fired reheat-regenerative power cycle to balance out the intermittency of wind power. System 3 incorporates hydrogen production, a solid oxide fuel cell for continuous electricity production, an absorption cooling system for refrigeration, and a heat exchanger for hot water production. These systems are modeled with Engineering Equation Solver, and analyzed based on energy and exergy efficiencies, and on economic metrics like levelized cost of electricity (LCOE), cooling (LCOC), refrigeration (LCOR), and hydrogen (LCOH) under steady-state conditions. A sensitivity analysis of various parameters is presented to assess the change in performance. Systems were optimized using a multi-objective method, with maximizing exergy efficiency and minimizing total product unit cost used as objective functions. The results show that System 1 achieves 79.78 % energy efficiency and 53.94 % exergy efficiency. System 2 achieves efficiencies of 55.26 % and 27.05 % respectively, while System 3 attains 78.73 % and 58.51 % respectively. The levelized costs for micro-multi-energy System 1 are LCOE = 0.04993 $/kWh, LCOC = 0.004722$/kWh, and LCOH = 0.03328 $/kWh. For System 2, these values are 0.03653$/kWh, 0.003743 $/kWh, and 0.03328$/kWh. In the case of System 3, they are 0.03736 $/kWh, 0.004726$/kWh, and 0.03335 $/kWh, and LCOR = 0.03309$/kWh. The results show that the systems modeled here have competitive performance with existing multi-energy systems, powered by other renewables. Integrating these systems will further the sustainable and net zero energy system transition, especially in rural communities.</p

Different patterns of NF-κB and Notch1 signaling contribute to tumor-induced lymphangiogenesis of esophageal squamous cell carcinoma

<p>Abstract</p> <p>Background</p> <p>Lymph node involvement and tumor-induced lymphangiogenesis appear as the earliest features of esophageal squamous cell carcinoma (ESCC), although the molecular regulatory mechanisms involved have remained unclear. Our aim was to investigate the contribution of NF-κB and Notch1 signaling to lymph node involvement and tumor-induced lymphangiogenesis in ESCC.</p> <p>Material and methods</p> <p>NF-κB and Notch1 expression in 60 tissue samples of ESCC were assessed by immunohistochemical staining. The correlations of NF-κB and Notch1 with lymph node involvement, lymphatic vessel density (LVD), podoplanin, and vascular endothelial growth factor-C (VEGF-C) were further evaluated to determine the association of NF-κB and Notch1 expression with tumor-induced lymphangiogenesis.</p> <p>Results</p> <p>Chi-square tests revealed that NF-κB and Notch1 expression in ESCC tissues were significant associated with lymph node metastasis, LVD, podoplanin, and VEGF-C expression. Strong expression of NF-κB, but weak expression of Notch1, was observed in tumor tissues with lymph nodes involvement (<it>P </it>< 0.05 for both). The mean histoscores of LVD, podoplanin, and VEGF-C staining were higher in high-NF-κB-expressing tissue than in low-expressing tissue (<it>P </it>< 0.05 for each). In contrast, the mean histoscores of LVD and VEGF-C staining were lower in high-Notch1-expressing tissue than in low-expressing tissue (<it>P </it>< 0.05 for both). A multiple factors analysis of LVD and VEGF-C further demonstrated that LVD and VEGF-C status were significantly correlated with NF-κB and Notch1 expression in tumors. NF-κB and Notch1 expression were also significantly inversely correlated (<it>P </it>< 0.05).</p> <p>Conclusion</p> <p>These results suggest that different patterns of NF-κB and Notch1 signaling contribute to lymph nodes metastasis and tumor-induced lymphangiogenesis of ESCC, and reveal that up-regulation of NF-κB is associated with down-regulation of Notch1 in tumor tissue.</p

Robust Mixture-of-Expert Training for Convolutional Neural Networks

Sparsely-gated Mixture of Expert (MoE), an emerging deep model architecture, has demonstrated a great promise to enable high-accuracy and ultra-efficient model inference. Despite the growing popularity of MoE, little work investigated its potential to advance convolutional neural networks (CNNs), especially in the plane of adversarial robustness. Since the lack of robustness has become one of the main hurdles for CNNs, in this paper we ask: How to adversarially robustify a CNN-based MoE model? Can we robustly train it like an ordinary CNN model? Our pilot study shows that the conventional adversarial training (AT) mechanism (developed for vanilla CNNs) no longer remains effective to robustify an MoE-CNN. To better understand this phenomenon, we dissect the robustness of an MoE-CNN into two dimensions: Robustness of routers (i.e., gating functions to select data-specific experts) and robustness of experts (i.e., the router-guided pathways defined by the subnetworks of the backbone CNN). Our analyses show that routers and experts are hard to adapt to each other in the vanilla AT. Thus, we propose a new router-expert alternating Adversarial training framework for MoE, termed AdvMoE. The effectiveness of our proposal is justified across 4 commonly-used CNN model architectures over 4 benchmark datasets. We find that AdvMoE achieves 1% ~ 4% adversarial robustness improvement over the original dense CNN, and enjoys the efficiency merit of sparsity-gated MoE, leading to more than 50% inference cost reduction. Codes are available at https://github.com/OPTML-Group/Robust-MoE-CNN.Comment: ICCV 202

Real-time face view correction for front-facing cameras

Face view is particularly important in person-to-person communication. Disparity between the camera location and the face orientation can result in undesirable facial appearances of the participants during video conferencing. This phenomenon becomes particularly notable on devices where the front-facing camera is placed at unconventional locations such as below the display or within the keyboard. In this paper, we takes the video stream from a single RGB camera as input, and generates a video stream that emulates the view from a virtual camera at a designated location. The most challenging issue of this problem is that the corrected view often needs out-of-plane head rotations. To address this challenge, we reconstruct 3D face shape and re-render it into synthesized frames according to the virtual camera location. To output the corrected video stream with natural appearance in real-time, we propose several novel techniques including accurate eyebrow reconstruction, high-quality blending between corrected face image and background, and a template-based 3D reconstruction of glasses. Our system works well for different lighting conditions and skin tones, and is able to handle users wearing glasses. Extensive experiments and user studies demonstrate that our proposed method can achieve high-quality results