Methanesulfonate in the firn of King George Island, Antarctica

Methanesulfonate was investigated as a potential contributor to the sulfur budget, based on analysis of a firn core from Collins Ice Cap, King George Island, Antarctica (62°10′ S, 58°50′ W). The anion was found to be present at a mean concentration of 0.17 μeq L−1, with a maximum of 0.73 μeq L−1. Dating based on the δ 18O profile suggests that the principal peaks of methanesulfonate are associated with snow deposited in summer and autumn. A careful examination of MSA, SO4 2− and nssSO4 2− profiles indicates that two of the three peaks in the MSA profile may result mainly from migration and relocation of MSA. The mechanism responsible for this might be similar to that for deep cores from other Antarctic glaciers, supporting the migration hypothesis proposed by prior researchers and extending it to near-temperate ice. Due to the post-depositional modification, the main part of the MSA profile of the firn is no longer indicative of the seasonal pattern of MSA in the atmosphere, and the basis for calculation of the MSA/nssSO4 2− ratio should be changed. The MSA/nssS04 2 ratio obtained by a new computation is 0.22, 10% higher than that ignoring the effect of MSA migration

Quantum Approximate Optimization Algorithm Parameter Prediction Using a Convolutional Neural Network

The Quantum approximate optimization algorithm (QAOA) is a quantum-classical hybrid algorithm aiming to produce approximate solutions for combinatorial optimization problems. In the QAOA, the quantum part prepares a quantum parameterized state that encodes the solution, where the parameters are optimized by a classical optimizer. However, it is difficult to find optimal parameters when the quantum circuit becomes deeper. Hence, there is numerous active research on the performance and the optimization cost of QAOA. In this work, we build a convolutional neural network to predict parameters of depth QAOA instance by the parameters from the depth QAOA counterpart. We propose two strategies based on this model. First, we recurrently apply the model to generate a set of initial values for a certain depth QAOA. It successfully initiates depth 10 QAOA instances, whereas each model is only trained with the parameters from depths less than 6. Second, the model is applied repetitively until the maximum expected value is reached. An average approximation ratio of 0.9759 for Max-Cut over 264 Erd\H{o}s-R\'{e}nyi graphs is obtained, while the optimizer is only adopted for generating the first input of the model.Comment: 9 pages, 4 figures, 1 table

The role of GLI2-ABCG2 signaling axis for 5Fu resistance in gastric cancer

Gastric cancer is a leading cause of cancer-related mortality worldwide, and options to treat gastric cancer are limited. Fluorouracil (5Fu)-based chemotherapy is frequently used as a neoadjuvant or an adjuvant agent for gastric cancer therapy. Most patients with advanced gastric cancer eventually succumb to the disease despite the fact that some patients respond initially to chemotherapy. Thus, identifying molecular mechanisms responsible for chemotherapy resistance will help design novel strategies to treat gastric cancer. In this study, we discovered that residual cancer cells following 5Fu treatment have elevated expression of hedgehog (Hg) target genes GLI1 and GLI2, suggestive of Hh signaling activation. Hh signaling, a pathway essential for embryonic development, is an important regulator for putative cancer stem cells/residual cancer cells. We found that high GLI1/GLI2 expression is associated with some features of putative cancer stem cells, such as increased side population. We demonstrated that GLI2 knockdown sensitized gastric cancer cells to 5Fu treatment, decreased ABCG2 expression, and reduced side population. Elevated GLI2 expression is also associated with an increase in tumor sphere size, another marker for putative cancer stem cells. We believe that GLI2 regulates putative cancer stem cells through direct regulation of ABCG2. ABCG2 can rescue the GLI2 shRNA effects in 5Fu response, tumor sphere formation and side population changes, suggesting that ABCG2 is an important mediator for GLI2-associated 5Fu resistance. The relevance of our studies to gastric cancer patient care is reflected by our discovery that high GLI1/GLI2/ABCG2 expression is associated with a high incidence of cancer relapse in two cohorts of gastric cancer patients who underwent chemotherapy (containing 5Fu). Taken together, we have identified a molecular mechanism by which gastric cancer cells gain 5Fu resistance

Characteristics of the Tan-Lu Strike-Slip Fault and Its Controls on Hydrocarbon Accumulation in the Liaodong Bay Sub-Basin, Bohai Bay Basin, China

The Tan-Lu Fault, one of the major strike-slip structures in China, controlled the development of most of the Meso-Cenozoic NNE trend rifted petroliferous basins in east China. It has cut across the Bohai Bay Basin since the late Cenozoic and played an important role in hydrocarbon accumulation and distribution in the Liaodong Bay sub-basin of the Bohai Bay Basin. The purpose of this paper is to study the geometry of the Tan-Lu strike-slip and how it affected petroleum system development in the Liaodong Bay sub-basin. The innovative seismic interpretation revealed the western branch of the Tan-Lu strike-slip fault cut through the Liaozhong depression of the sub-basin and its eastern branch superimposed on the earlier extensional boundary fault of the sub-basin. The strike-slip movement is characterized by a distinctive strike-slip zone associated with the NE en echelon faults in the central part of the Liaozhong depression and also caused the formation of the Liaodong uplift and the Liaodong depression in the east Liaodong Bay Sub-basin. Rapid movement of the Tan-Lu strike-slip fault has deepened the Liaozhong depression and facilitated the maturation of source rock. Related fault movement formed a series of structural traps and paleotopographic highs and lows that subsequently controlled sediment dispersal and the distribution of stratigraphic-related traps within sequence stratigraphic framework. Exploration practice, geochemical study and petroleum system modeling demonstrate that the Tan-Lu strike-slip and its associated faults acted as good hydrocarbon migration pathways and hydrocarbon accumulated in many traps associated with the Tan-Lu strike-slip zone. Many recent discoveries along the strike-slip zone prove that the petroleum system in Liaodong Bay Sub-basin was mainly controlled by the activity of the Tan-Lu strike-slip. The resulting hydrocarbon accumulation model in this sub-basin may provide a paradigm for the prediction of hydrocarbon accumulation to other east China basins along the Tan-Lu strike-slip fault zone. Key words: Liaodong Bay Sub-basin; Tan-Lu strike-slip fault; Hydrocarbon accumulation; Petroleum system; Sequence stratigraph

USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentation

Seed area generation is usually the starting point of weakly supervised semantic segmentation (WSSS). Computing the Class Activation Map (CAM) from a multi-label classification network is the de facto paradigm for seed area generation, but CAMs generated from Convolutional Neural Networks (CNNs) and Transformers are prone to be under- and over-activated, respectively, which makes the strategies to refine CAMs for CNNs usually inappropriate for Transformers, and vice versa. In this paper, we propose a Unified optimization paradigm for Seed Area GEneration (USAGE) for both types of networks, in which the objective function to be optimized consists of two terms: One is a generation loss, which controls the shape of seed areas by a temperature parameter following a deterministic principle for different types of networks; The other is a regularization loss, which ensures the consistency between the seed areas that are generated by self-adaptive network adjustment from different views, to overturn false activation in seed areas. Experimental results show that USAGE consistently improves seed area generation for both CNNs and Transformers by large margins, e.g., outperforming state-of-the-art methods by a mIoU of 4.1% on PASCAL VOC. Moreover, based on the USAGE-generated seed areas on Transformers, we achieve state-of-the-art WSSS results on both PASCAL VOC and MS COCO

Upregulation of Src homology phosphotyrosyl phosphatase 2 (Shp2) expression in oral cancer and knockdown of Shp2 expression inhibit tumor cell viability and invasion in vitro

ObjectiveThis study investigated the clinical significance of Shp2 protein expression in oral squamous cell carcinoma (OSCC) and elucidated its biologic significance in OSCC cells.Study DesignA total of 88 OSCC cases were used to assess Shp2 expression, out of which 70 were for immunohistochemistry and 18 paired tumors vs normal tissues were for Western blot of Shp2 expression. OSCC cells were used to assess the effects of Shp2 knockdown for cell viability, apoptosis, invasion, and protein expressions.ResultsExpression of Shp2 protein was significantly upregulated in OSCC tissues compared with the normal tissues, and Shp2 overexpression was associated with advanced tumor clinical stages and lymph node metastasis ex vivo. Knockdown of Shp2 expression in vitro inhibited OSCC cell viability and invasion but induced apoptosis by regulating expression of the apoptosis-related proteins.ConclusionsThe data indicated that Shp2 may play an important role in OSCC progression. Further studies will investigate whether a target of Shp2 expression could be a novel therapeutic strategy for clinical control of OSCC

GLI1-mediated regulation of side population is responsible for drug resistance in gastric cancer

Gastric cancer is the third leading cause of cancer-related mortality worldwide. Chemotherapy is frequently used for gastric cancer treatment. Most patients with advanced gastric cancer eventually succumb to the disease despite some patients responded initially to chemotherapy. Thus, identifying molecular mechanisms responsible for cancer relapse following chemotherapy will help design new ways to treat gastric cancer. In this study, we revealed that the residual cancer cells following treatment with chemotherapeutic reagent cisplatin have elevated expression of hedgehog target genes GLI1, GLI2 and PTCH1, suggestive of hedgehog signaling activation. We showed that GLI1 knockdown sensitized gastric cancer cells to CDDP whereas ectopic GLI1 expression decreased the sensitivity. Further analyses indicate elevated GLI1 expression is associated with an increase in tumor sphere formation, side population and cell surface markers for putative cancer stem cells. We have evidence to support that GLI1 is critical for maintenance of putative cancer stem cells through direct regulation of ABCG2. In fact, GLI1 protein was shown to be associated with the promoter fragment of ABCG2 through a Gli-binding consensus site in gastric cancer cells. Disruption of ABCG2 function, through ectopic expression of an ABCG2 dominant negative construct or a specific ABCG2 inhibitor, increased drug sensitivity of cancer cells both in culture and in mice. The relevance of our studies to gastric cancer patient care is reflected by our discovery that high ABCG2 expression was associated with poor survival in the gastric cancer patients who underwent chemotherapy. Taken together, we have identified a molecular mechanism by which gastric cancer cells gain chemotherapy resistance

Iterative Layerwise Training for Quantum Approximate Optimization Algorithm

The capability of the quantum approximate optimization algorithm (QAOA) in solving the combinatorial optimization problems has been intensively studied in recent years due to its application in the quantum-classical hybrid regime. Despite having difficulties that are innate in the variational quantum algorithms (VQA), such as barren plateaus and the local minima problem, QAOA remains one of the applications that is suitable for the recent noisy intermediate scale quantum (NISQ) devices. Recent works have shown that the performance of QAOA largely depends on the initial parameters, which motivate parameter initialization strategies to obtain good initial points for the optimization of QAOA. On the other hand, optimization strategies focus on the optimization part of QAOA instead of the parameter initialization. Instead of having absolute advantages, these strategies usually impose trade-offs to the performance of the optimization problems. One of such examples is the layerwise optimization strategy, in which the QAOA parameters are optimized layer-by-layer instead of the full optimization. The layerwise strategy costs less in total compared to the full optimization, in exchange of lower approximation ratio. In this work, we propose the iterative layerwise optimization strategy and explore the possibility for the reduction of optimization cost in solving problems with QAOA. Using numerical simulations, we found out that by combining the iterative layerwise with proper initialization strategies, the optimization cost can be significantly reduced in exchange for a minor reduction in the approximation ratio. We also show that in some cases, the approximation ratio given by the iterative layerwise strategy is even higher than that given by the full optimization.Comment: 9 pages, 3 figure

A Feasibility-Preserved Quantum Approximate Solver for the Capacitated Vehicle Routing Problem

The Capacitated Vehicle Routing Problem (CVRP) is an NP-optimization problem (NPO) that arises in various fields including transportation and logistics. The CVRP extends from the Vehicle Routing Problem (VRP), aiming to determine the most efficient plan for a fleet of vehicles to deliver goods to a set of customers, subject to the limited carrying capacity of each vehicle. As the number of possible solutions skyrockets when the number of customers increases, finding the optimal solution remains a significant challenge. Recently, a quantum-classical hybrid algorithm known as Quantum Approximate Optimization Algorithm (QAOA) can provide better solutions in some cases of combinatorial optimization problems, compared to classical heuristics. However, the QAOA exhibits a diminished ability to produce high-quality solutions for some constrained optimization problems including the CVRP. One potential approach for improvement involves a variation of the QAOA known as the Grover-Mixer Quantum Alternating Operator Ansatz (GM-QAOA). In this work, we attempt to use GM-QAOA to solve the CVRP. We present a new binary encoding for the CVRP, with an alternative objective function of minimizing the shortest path that bypasses the vehicle capacity constraint of the CVRP. The search space is further restricted by the Grover-Mixer. We examine and discuss the effectiveness of the proposed solver through its application to several illustrative examples.Comment: 9 pages, 8 figures, 1 tabl