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In monarchE, adjuvant abemaciclib significantly improved invasive disease-free survival (IDFS) and distant relapse-free survival (DRFS), with sustained benefit beyond the 2-year treatment period. Abemaciclib dose reductions were allowed to proactively manage adverse events. Exploratory analyses to investigate the impact of dose reductions on efficacy were conducted. Across the three patient subgroups as defined by relative dose intensity (≤66%, 66-93%, ≥93%), the estimated 4-year IDFS rates were generally consistent (87.1%, 86.4%, and 83.7%, respectively). In the time-dependent Cox proportional hazard model, the effect of abemaciclib was consistent at the full dose compared to being reduced to a lower dose (IDFS hazard ratio: 0.905; 95% confidence interval: 0.727, 1.125; DRFS hazard ratio: 0.942; 95% confidence interval: 0.742, 1.195). These analyses showed that the efficacy of adjuvant abemaciclib was not compromised by protocol mandated dose reductions for patients with node positive, hormone receptor positive, human epidermal growth factor 2-negative, high-risk early breast cancer.

Two different low molecular cryoprotectants were analyzed in silver carp surimi proteins during different frozen storage conditions (FTC), and a comparison was performed with a conventional cryoprotectant mixture of sucrose (4%) and sorbitol (4%) as positive control (PC). The results showed that a significant decline was noted in sulfhydryl contents (SH), an increase in carbonyl groups, and a surface hydrophobicity due to myosin and protein oxidation. Meanwhile, FTC also altered the stability of secondary and tertiary structures due to the denaturative changes in myofibrillar proteins (MP). Meanwhile, samples that incorporated KC (3%), KOG (3%), and PC showed significant stability in MP against FTC induced changes. This is due to their strong hydrophilic and hydrogen interactions. MP samples containing KOG (3%) demonstrated improved cryoprotective abilities against oxidative and structural changes. Thus, we can recommend KOG as a potential cryoprotectant for maintaining the shelf-life of fish and related products on an industrial scale., Zhejiang Province, King Saud University (Arabia Saudí), Natural Science Foundation of Zhejiang Province, National Natural Science Foundation of China, Sección Deptal. de Farmacia Galénica y Tecnología Alimentaria (Veterinaria), Fac. de Veterinaria, TRUE, pub

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical trial updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.Two years of adjuvant abemaciclib combined with endocrine therapy (ET) resulted in a significant improvement in invasive disease-free survival (IDFS) and distant relapse-free survival (DRFS) that persisted beyond the 2-year treatment period in patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative, node-positive, high-risk early breast cancer (EBC). Here, we report 5-year efficacy results from a prespecified overall survival (OS) interim analysis. In the intent-to-treat population, with a median follow-up of 54 months, the benefit of abemaciclib was sustained with hazard ratios of 0.680 (95% CI, 0.599 to 0.772) for IDFS and 0.675 (95% CI, 0.588 to 0.774) for DRFS. This persistence of abemaciclib benefit translated to continuous separation of the curves with a deepening in 5-year absolute improvement in IDFS and DRFS rates of 7.6% and 6.7%, respectively, compared with rates of 6% and 5.3% at 4 years and 4.8% and 4.1% at 3 years. With fewer deaths in the abemaciclib plus ET arm compared with the ET-alone arm (208 v 234), statistical significance was not reached for OS. No new safety signals were observed. In conclusion, abemaciclib plus ET continued to reduce the risk of developing invasive and distant disease recurrence beyond the completion of treatment. The increasing absolute improvement at 5 years is consistent with a carryover effect and further supports the use of abemaciclib in patients with high-risk EBC.

Long-Fei Xia,1 Jing-Bo Li,2 Guo-Sheng Tian,2 Wei-Ran Jiang,3 Yao-Shuang Li,1 Chen-Ying Lin,1 Hui-Na Qiu,2 Fan Wu,4 Jun-Jia Wang,5 Chun-Jun Li,2 Jing-Na Lin2 1Tianjin Union Medical Center, Tianjin Medical University, Tianjin, 300070, People’s Republic of China; 2Department of Endocrinology, Health Management Center, Tianjin Union Medical Center, Nankai University Affiliated Hospital, Tianjin, 300121, People’s Republic of China; 3Eastman Institute for Oral Health, University of Rochester Medical Center, Rochester, NY, USA; 4School of Medicine, Nankai University, Tianjin, 300071, People’s Republic of China; 5Graduate School, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People’s Republic of ChinaCorrespondence: Jing-Na Lin; Chun-Jun Li, Email 13207628978@163.com; li_chunjun@126.comObjective: To investigate the impact of sarcopenia on the 10-year risk of atherosclerotic cardiovascular disease (ASCVD) among individuals with type 2 diabetes mellitus (T2DM).Methods: This study included the clinical, laboratory, and body composition data of 1491 patients with T2DM who were admitted to the Department of Endocrinology and Metabolism at Tianjin Union Medical Center from July 2018 to July 2023. The China-PAR model was utilized to evaluate cardiovascular disease risk. Associations between ASCVD risk and various clinical parameters were analyzed, and the relationship between body composition parameters and ASCVD risk was assessed using logistic regression.Results: The analysis revealed that T2DM patients with sarcopenia had a higher 10-year ASCVD risk compared to those without sarcopenia, with reduced muscle mass independently predicting an increased risk of cardiovascular disease. This association was significant among female T2DM patients, while male T2DM patients with sarcopenia showed a marginally higher median ASCVD risk compared to their non-sarcopenic counterparts. ASCVD risk inversely correlated with body muscle parameters and positively cor

As global concerns about climate change intensify, the transition towards zero-emission freight is becoming increasingly vital. Drayage is an important segment of the freight system, typically involving the transport of goods from seaports or intermodal terminals to nearby warehouses. This sector significantly contributes to not only greenhouse gas emissions, but also pollution in densely populated areas. This study presents a holistic optimization model designed for an efficient transition to zero-emission drayage, offering cost-effective strategies for the coordinated investment planning for power systems, charging infrastructure, and electric drayage trucks. The model is validated in the Greater Los Angeles area, where regulatory goals are among the most ambitious. Furthermore, the model's design allows for easy adaptation to other regions. By focusing on drayage trucks, this study also paves the way for future research into other freight categories, establishing a foundation for a more extensive exploration in this field., Comment: 34 pages, 10 figures

Assurance cases are used to communicate and assess confidence in critical system properties such as safety and security. Historically, assurance cases have been manually created documents, which are evaluated by system stakeholders through lengthy and complicated processes. In recent years, model-based system assurance approaches have gained popularity to improve the efficiency and quality of system assurance activities. This becomes increasingly important, as systems becomes more complex, it is a challenge to manage their development life-cycles, including coordination of development, verification and validation activities, and change impact analysis in inter-connected system assurance artifacts. Moreover, there is a need for assurance cases that support evolution during the operational life of the system, to enable continuous assurance in the face of an uncertain environment, as Robotics and Autonomous Systems (RAS) are adopted into society. In this paper, we contribute ACCESS - Assurance Case Centric Engineering of Safety-critical Systems, an engineering methodology, together with its tool support, for the development of safety critical systems around evolving model-based assurance cases. We show how model-based system assurance cases can trace to heterogeneous engineering artifacts (e.g. system architectural models, system safety analysis, system behaviour models, etc.), and how formal methods can be integrated during the development process. We demonstrate how assurance cases can be automatically evaluated both at development and runtime. We apply our approach to a case study based on an Autonomous Underwater Vehicle (AUV).

The concept of Digital Twin (DT) is increasingly applied to systems on different levels of abstraction across domains, to support monitoring, analysis, diagnosis, decision making and automated control. Whilst the interest in applying DT is growing, the definition of DT is unclear, neither is there a clear pathway to develop DT to fully realise its capacities. In this paper, we revise the concept of DT and its categorisation. We propose a DT maturity matrix, based on which we propose a model-based DT development methodology. We also discuss how model-based tools can be used to support the methodology and present our own supporting tool. We report our preliminary findings with a discussion on a case study, in which we use our proposed methodology and our supporting tool to develop an extensible DT platform for the assurance of Electrical and Electronics systems of space launch vehicles.

Graph is considered a promising way for managing building information. A new graphic form of IFC (Industry Foundation Classes) data has just been developed, referred to as IFC-Graph. However, understanding of IFC-Graph is insufficient, especially for information query. This study aims to explore graphic building information query and develop a graph query language tailored for IFC-Graph. A series of tasks were carried out, including a) investigating the structure of IFC data and the main types of information in IFC, b) investigating the graph query language Cypher, and c) developing a set of tailored functional query patterns. The developed language is referred to as Cypher4BIM. Five IFC models were used for validation, and the result shows that Cypher4BIM can query individual instances and complex relations from IFC, such as spatial structure, space boundary, and space accessibility. This study contributes to applications that require effective building information query, such as digital twin.

Severe weather events such as floods, hurricanes, earthquakes, and large wind or ice storms can cause extensive damage to electrical distribution networks, requiring a multi-day restoration effort. Complicating the recovery process is the lack of complete and accurate information regarding the extent and locations of damages, at least during the initial part of the recovery process. These factors make workforce planning challenging. In this paper, we adopt a rolling horizon restoration framework whereby repairs are planned for adjustable finite length restoration windows. Considering both repair times as well as travel times, we show that the optimal scheduling problem with multiple crews, each with their own time budget, can be recast in terms of a cost constrained reward maximizing mTSP (traveling salesman problem) on doubly weighted graphs, where the objective is to maximize the aggregate reward earned during the upcoming restoration window, provided no crew violates its time budget and certain electrical continuity constraints are met. We propose a mixed integer linear programming (MILP) model for solving the above problem which is validated on standard IEEE PES test feeder networks., Comment: 26 pages, 17 figures

Objective- To identify and assess whether three major risk factors that due to differential access to flexible resources might help explain disparities in the spread of COVID-19 across communities with different socioeconomic status, including socioeconomic inequalities in social distancing, the potential risk of interpersonal interactions, and access to testing.MethodsAnalysis uses ZIP code level weekly COVID-19 new cases, weekly population movement flows, weekly close-contact index, and weekly COVID-19 testing sites in Southern California from March 2020 to April 2021, merged with the U.S. census data to measure ZIP code level socioeconomic status and cofounders. This study first develops the measures for social distancing, the potential risk of interactions, and access to testing. Then we employ a spatial lag regression model to quantify the contributions of those factors to weekly COVID-19 case growth.ResultsResults identify that, during the first COVID-19 wave, new case growth of the low-income group is two times higher than that of the high-income group. The COVID-19 case disparity widens to four times in the second COVID-19 wave. We also observed significant disparities in social distancing, the potential risk of interactions, and access to testing among communities with different socioeconomic status. In addition, all of them contribute to the disparities of COVID-19 incidences. Among them, the potential risk of interactions is the most important contributor, whereas testing accessibility contributes least. We also found that close-contact is a more effective measure of social distancing than population movements in examining the spread of COVID-19.Conclusion- This study answers critically unaddressed questions about health disparities in the spread of COVID-19 by assessing factors that might explain why the spread is different in different groups.

The public and private sectors are making significant investments in electric vehicle charging infrastructure, however, the design, planning, and rollout of charging infrastructure tends to be siloed by mode (i.e., charging needs for passenger vehicles, transit buses, trucks, e-scooters, and other modes are not considered together). Given the ever-increasing interactions among these electrified transportation modes, there is growing interest in how to better integrate the design and delivery of charging infrastructure, such as through shared charging hubs.To help policy makers and transportation agencies make more informed decisions regarding the planning and design of charging infrastructure, UC Riverside researchers developed a model for determining the optimal location, configuration (i.e., number of chargers, power capacities), and charging schedules for shared charging hubs serving cars and transit buses with the goal of reducing greenhouse gas (GHG) emissions. The model was then applied using Contra Costa County in California as a case study.

With the growing concern over global climate change, the pace of transportation electrification has greatly accelerated in recent years to achieve net-zero greenhouse gas (GHG) emissions. However, electricity is not entirely generated from renewable resources at the moment. The overall carbon emission per kWh of electricity fluctuates due to the inconsistent nature of renewables such as solar and wind. How to utilize the low-carbon electricity remains a challenge. On the other hand, current designs and research on charging infrastructure separate electric buses and passenger cars, neglecting the great potential of coordinated charging between different transport modes. How to deploy and operate public charging infrastructure to best serve an electrified multi-modal transportation system while maximizing the benefits of decarbonization remains unclear. This research tries to integrate different transport modes into the strategic planning and design of the shared charging hubs to produce an efficient and low-carbon electrified transportation ecosystem. Shared charging hubs can provide holistic energy management to maximize GHG emission reduction given budget limits while balance peak power demands by integrating real-time electricity carbon intensity (ECI) and vehicle-to-grid (V2G) technology. The model was successfully applied to bus fleets of seven transit agencies and the park-and-ride cars of twelve rail transit stations in Contra Costa County, California. This research will help policy makers and transportation agencies make more informed decisions regarding the planning and design of charging infrastructure.

All systems rely on inputs and outputs (I/Os) to perceive and interact with their surroundings. In safety-critical systems, it is important to guarantee both the performance and time-predictability of I/O operations. However, with the continued growth of architectural complexity in modern safety-critical systems, satisfying such real-time requirements has become increasingly challenging due to complex I/O transaction paths and extensive hardware contention. In this paper, we present a new NoC-based Predictable I/O system framework (NPRC-I/O) which reduces this contention and ensures the performance and timepredictability of I/O operations. Specifically, NPRC-I/O contains a programmable I/O command controller (NPRC-CC) and a runtime reconfigurable NoC (RNoC), which provides the capability to adjust I/O transaction paths at run-time. Using this flexibility, we construct an end-to-end transmission latency analysis and an optimisation engine that produces configurations for NPRCI/ O and the I/O traffic in a given system. The constructed analysis and optimisation engine guarantee the timing of all hard realtime traffic while reducing the deadline misses of soft real-time traffic and overall transmission latency.

Yao-Shuang Li,1,2,* Yan-Lan Liu,2,* Jun-Jia Wang,2,3 Nahal Haghbin,4 Xiao-He Wang,5 Wei-Ran Jiang,6 Hui-Na Qiu,2 Long-Fei Xia,1,2 Fan Wu,2,7 Chen-Ying Lin,1,2 Jing-Bo Li,2 Jing-Na Lin2 1Tianjin Union Medical Center, Tianjin Medical University, Tianjin, People’s Republic of China; 2Department of Endocrinology, Tianjin Union Medical Center, Nankai University Affiliated Hospital, Tianjin, People’s Republic of China; 3Tianjin Union Medical Center, Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China; 4School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 5Institute of Non-Communicable Diseases Control and Prevention, Tianjin Center for Disease Control and Prevention, Tianjin, People’s Republic of China; 6Eastman Institute for Oral Health, University of Rochester Medical Center, Rochester, NY, USA; 7Tianjin Union Medical Center, School of Medicine, Nankai University, Tianjin, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jing-Na Lin; Jing-Bo Li, Email 13207628978@163.com; doctorljb@aliyun.comObjective: The aim of this study was to elucidate the relationship between specific body composition and the risk of Cognitive Impairment (CI) in middle-aged Type 2 Diabetes Mellitus (T2DM) patients.Methods: This cross-sectional study included 504 hospitalized patients with T2DM from the Department of Endocrinology and Metabolism of the Tianjin Union Medical Center. Subjects were grouped by sex, and cognitive status was assessed using the Montreal Cognitive Assessment (MoCA). The relationship between body composition and cognitive ability was investigated with the use of linear regression analysis. The association between body composition and CI risk was determined by logistic regression analysis.Results: The prevalence of CI was 39.3% in middle-aged T2DM patients. Af

Spatially variable (SV) gene detection plays a crucial role in analyzing spatial transcriptomicsdata for dissecting complex tissue microenvironments. Recently, a novel weighted leverage score (WLS) variable screening method was proposed to efficiently identify important features. Herein, this research introduced a new approach applying the WLS method together with a clustering algorithm, such as BayesSpace, to effectively identify SV genes. Through rigorous testing on simulated and rea-world datasets against established methods, the combination of the WLS method and BayesSpace consistently demonstrated superior accuracy in SV gene detection. As a robust, efficient, and flexible tool, the WLS method can be used to significantly improves the results in studying complex relationships between molecular cell functions and tissue phenotypes in spatial transcriptomics analysis.

Timeliness of information is critical for Basic Safety Messages (BSMs) in Vehicle-to-Everything (V2X) communication to enable highly reliable autonomous driving. However, the current semi-persistent scheduling (SPS) algorithms in the 5th generation New Radio (5G NR) standard can still generate collisions probability, close to 20% with 100 vehicles per kilometer, such that they cannot meet this requirement for BSMs. This paper proposes a Ledger concept, which can communicate collision information to every vehicle in the network within one Resource Reservation Interval (RRI) through the broadcasting of Ledger information. The colliding vehicle is aware that it has collided with other vehicles and will reselect for the next SPS period. Besides that, other protocols follow the SPS. Although it sacrifices 14.29% of resources by including Ledger, it can eventually reduce the collision probability. In this paper, a Monte Carlo simulator is used to verify and analyze the performance of the Ledger system. Numerical results show that abide by the SPS protocol, the Ledger system can make the collision probability converge to zero after amount of RRIs., Comment: 6 pages, 4 figures

Existing point cloud learning methods aggregate features from neighbouring points relying on constructing graph in the spatial domain, which results in feature update for each point based on spatially-fixed neighbours throughout layers. In this paper, we propose a dynamic feature aggregation (DFA) method that can transfer information by constructing local graphs in the feature domain without spatial constraints. By finding k-nearest neighbors in the feature domain, we perform relative position encoding and semantic feature encoding to explore latent position and feature similarity information, respectively, so that rich local features can be learned. At the same time, we also learn low-dimensional global features from the original point cloud for enhancing feature representation. Between DFA layers, we dynamically update the constructed local graph structure, so that we can learn richer information, which greatly improves adaptability and efficiency. We demonstrate the superiority of our method by conducting extensive experiments on point cloud classification and segmentation tasks. Implementation code is available: https://github.com/jiamang/DFA., Comment: 14 pages , 4 figures , submitted to Signal Processing:image communications

Discovering inter-point connection for efficient high-dimensional feature extraction from point coordinate is a key challenge in processing point cloud. Most existing methods focus on designing efficient local feature extractors while ignoring global connection, or vice versa. In this paper, we design a new Inductive Bias-aided Transformer (IBT) method to learn 3D inter-point relations, which considers both local and global attentions. Specifically, considering local spatial coherence, local feature learning is performed through Relative Position Encoding and Attentive Feature Pooling. We incorporate the learned locality into the Transformer module. The local feature affects value component in Transformer to modulate the relationship between channels of each point, which can enhance self-attention mechanism with locality based channel interaction. We demonstrate its superiority experimentally on classification and segmentation tasks. The code is available at: https://github.com/jiamang/IBT

Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. Prior models developed from control theory and physics-based rules are limited in automated vehicle applications due to their restricted behavioral repertoire. Data-driven machine learning models are more capable than rule-based models but are limited by the need for large training datasets and their lack of interpretability, i.e., an understandable link between input data and output behaviors. We propose a novel car following modeling approach using active inference, which has comparable behavioral flexibility to data-driven models while maintaining interpretability. We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against the rule-based Intelligent Driver Model, and two neural network Behavior Cloning models. The models were trained and tested on a real-world driving dataset using a consistent process. The testing results showed that the AIDA predicted driving controls significantly better than the rule-based Intelligent Driver Model and had similar accuracy to the data-driven neural network models in three out of four evaluations. Subsequent interpretability analyses illustrated that the AIDA's learned distributions were consistent with driver behavior theory and that visualizations of the distributions could be used to directly comprehend the model's decision making process and correct model errors attributable to limited training data. The results indicate that the AIDA is a promising alternative to black-box data-driven models and suggest a need for further research focused on modeling driving style and model training with more diverse datasets.

Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adaptive driving behavior from data, due to their black box nature, they offer little or no explanation of the mechanisms underlying the adaptive behavior. Thus, a generalizable, interpretable, computational model of adaptive human driving behavior is still lacking. This paper proposes such a model based on active inference, a behavioral modeling framework originating in computational neuroscience. The model offers a principled solution to how humans trade progress against caution through policy selection based on the single mandate to minimize expected free energy. This casts goal-seeking and information-seeking (uncertainty-resolving) behavior under a single objective function, allowing the model to seamlessly resolve uncertainty as a means to obtain its goals. We apply the model in two apparently disparate driving scenarios that require managing uncertainty, (1) driving past an occluding object and (2) visual time sharing between driving and a secondary task, and show how human-like adaptive driving behavior emerges from the single principle of expected free energy minimization., Comment: 33 pages, 13 figures

Model-based Reinforcement Learning (MBRL) aims to make agents more sample-efficient, adaptive, and explainable by learning an explicit model of the environment. While the capabilities of MBRL agents have significantly improved in recent years, how to best learn the model is still an unresolved question. The majority of MBRL algorithms aim at training the model to make accurate predictions about the environment and subsequently using the model to determine the most rewarding actions. However, recent research has shown that model predictive accuracy is often not correlated with action quality, tracing the root cause to the objective mismatch between accurate dynamics model learning and policy optimization of rewards. A number of interrelated solution categories to the objective mismatch problem have emerged as MBRL continues to mature as a research area. In this work, we provide an in-depth survey of these solution categories and propose a taxonomy to foster future research.

We consider a Bayesian approach to offline model-based inverse reinforcement learning (IRL). The proposed framework differs from existing offline model-based IRL approaches by performing simultaneous estimation of the expert's reward function and subjective model of environment dynamics. We make use of a class of prior distributions which parameterizes how accurate the expert's model of the environment is to develop efficient algorithms to estimate the expert's reward and subjective dynamics in high-dimensional settings. Our analysis reveals a novel insight that the estimated policy exhibits robust performance when the expert is believed (a priori) to have a highly accurate model of the environment. We verify this observation in the MuJoCo environments and show that our algorithms outperform state-of-the-art offline IRL algorithms.

Epigallocatechin-3-gallate (EGCG), a major polyphenol component of green tea, presents anticancer efficacy. However, its exact mechanism of action is not known. In this study, we evaluated the effect of EGCG alone or in combination with current chemotherapeutics [gemcitabine, 5-flourouracil (5-FU), and doxorubicin] on pancreatic, colon, and lung cancer cell growth, as well as the mechanisms involved in the combined action. EGCG reduced pancreatic, colon, and lung cancer cell growth in a concentration and time-dependent manner. EGCG strongly induced apoptosis and blocked cell cycle progression. Moreover, EGCG enhanced the growth inhibitory effect of 5-FU and doxorubicin. Of note, EGCG enhanced 5-FU's and doxorubicin's effect on apoptosis, but not on cell cycle. Mechanistically, EGCG reduced ERK phosphorylation concentration-dependently, and sensitized gemcitabine, 5-FU, and doxorubicin to further suppress ERK phosphorylation in multiple cancer cell lines. In conclusion, EGCG presents a strong anticancer effect in pancreatic, colon, and lung cancer cells and is a robust combination partner for multiple chemotherapeutics as evidenced by reducing cancer cell growth, in part, by inhibiting the ERK pathway.

Epigallocatechin-3-gallate (EGCG), a major polyphenol component of green tea, presents anticancer efficacy. However, its exact mechanism of action is not known. In this study, we evaluated the effect of EGCG alone or in combination with current chemotherapeutics [gemcitabine, 5-flourouracil (5-FU), and doxorubicin] on pancreatic, colon, and lung cancer cell growth, as well as the mechanisms involved in the combined action. EGCG reduced pancreatic, colon, and lung cancer cell growth in a concentration and time-dependent manner. EGCG strongly induced apoptosis and blocked cell cycle progression. Moreover, EGCG enhanced the growth inhibitory effect of 5-FU and doxorubicin. Of note, EGCG enhanced 5-FU's and doxorubicin's effect on apoptosis, but not on cell cycle. Mechanistically, EGCG reduced ERK phosphorylation concentration-dependently, and sensitized gemcitabine, 5-FU, and doxorubicin to further suppress ERK phosphorylation in multiple cancer cell lines. In conclusion, EGCG presents a strong anticancer effect in pancreatic, colon, and lung cancer cells and is a robust combination partner for multiple chemotherapeutics as evidenced by reducing cancer cell growth, in part, by inhibiting the ERK pathway.

Novel therapeutic strategies are needed in the fight against pancreatic cancer. We have previously documented the chemopreventive effect of MDC-22 in preclinical models of pancreatic cancer. In the present work, we examined the therapeutic effects of MDC-22 in patient-derived tumor xenografts (PDTXs) and in LSL-KrasG12D/+, LSL-Trp53R172H/+, Pdx1-Cre (KPC) genetically engineered mice, two complementary and clinically relevant animal models of pancreatic cancer. In addition, we evaluated whether MDC-22 could synergize with current chemotherapeutic drugs used in the clinic. MDC-22 reduced the growth of various human pancreatic cancer cell lines in a concentration-dependent manner. In vivo, MDC-22 strongly reduced patient-derived pancreatic tumor xenograft growth by 50%, and extended survival of LSL-KrasG12D/+; LSL-Trp53R172H/+; Pdx1-Cre (KPC) mice by over a month (5.3 months versus 7.0 months). In both models, MDC-22 inhibited EGFR activation and its downstream signals, including ERK and FAK phosphorylation. In human pancreatic cancer cell lines, MDC-22 enhanced the growth inhibitory effect of irinotecan, and to a lesser degree those of gemcitabine and nab-paclitaxel. Normal human pancreatic epithelial cells were more resistant to the cytotoxic effects of, both, MDC-22 alone or in combination with irinotecan, indicating selectivity. Furthermore, MDC-22 enhanced irinotecan's effect on cell migration, in part, by inhibiting EGFR/FAK signaling. Collectively, our results indicate that MDC-22 is an effective anticancer drug in preclinical models of pancreatic cancer, and suggest that MDC-22 plus irinotecan as drug combination strategy for pancreatic cancer treatment, which warrants further evaluation.

Novel therapeutic strategies are needed in the fight against pancreatic cancer. We have previously documented the chemopreventive effect of MDC-22 in preclinical models of pancreatic cancer. In the present work, we examined the therapeutic effects of MDC-22 in patient-derived tumor xenografts (PDTXs) and in LSL-KrasG12D/+, LSL-Trp53R172H/+, Pdx1-Cre (KPC) genetically engineered mice, two complementary and clinically relevant animal models of pancreatic cancer. In addition, we evaluated whether MDC-22 could synergize with current chemotherapeutic drugs used in the clinic. MDC-22 reduced the growth of various human pancreatic cancer cell lines in a concentration-dependent manner. In vivo, MDC-22 strongly reduced patient-derived pancreatic tumor xenograft growth by 50%, and extended survival of LSL-KrasG12D/+; LSL-Trp53R172H/+; Pdx1-Cre (KPC) mice by over a month (5.3 months versus 7.0 months). In both models, MDC-22 inhibited EGFR activation and its downstream signals, including ERK and FAK phosphorylation. In human pancreatic cancer cell lines, MDC-22 enhanced the growth inhibitory effect of irinotecan, and to a lesser degree those of gemcitabine and nab-paclitaxel. Normal human pancreatic epithelial cells were more resistant to the cytotoxic effects of, both, MDC-22 alone or in combination with irinotecan, indicating selectivity. Furthermore, MDC-22 enhanced irinotecan's effect on cell migration, in part, by inhibiting EGFR/FAK signaling. Collectively, our results indicate that MDC-22 is an effective anticancer drug in preclinical models of pancreatic cancer, and suggest that MDC-22 plus irinotecan as drug combination strategy for pancreatic cancer treatment, which warrants further evaluation.

The packet throughput in 5th Generation (5G) New Radio (NR) Vehicle-to-Everything (V2X) is highly dependent on the Medium Access Control (MAC) based scheduling algorithm with no base station participation. In particular, the Semi-Persistent Scheduling (SPS) algorithm has been standardized by the 3rd Generation Partnership Project (3GPP) for V2X resource scheduling in the out-of-coverage scenario. This paper analyzes the NR-V2X SPS throughput from the MAC perspective, where the packet reception ratio (PRR) and half-duplex (HD) effect dominate. We first investigate the average throughput in the fully connected vehicular network, in which all the vehicles share the same throughput. Subsequently, the average throughput as a function of distance in the partially connected vehicular network is analyzed. The Monte Carlo simulation results show that increasing the resource keeping probability can improve the average throughput. Meanwhile, in the partially connected network, the lower resource keeping probability is prone to obtaining the higher throughput gain by increasing the number of subchannels., Comment: 7 pages, 6 figures

Motion estimation and motion compensation are indispensable parts of inter prediction in video coding. Since the motion vector of objects is mostly in fractional pixel units, original reference pictures may not accurately provide a suitable reference for motion compensation. In this paper, we propose a deep reference picture generator which can create a picture that is more relevant to the current encoding frame, thereby further reducing temporal redundancy and improving video compression efficiency. Inspired by the recent progress of Convolutional Neural Network(CNN), this paper proposes to use a dilated CNN to build the generator. Moreover, we insert the generated deep picture into Versatile Video Coding(VVC) as a reference picture and perform a comprehensive set of experiments to evaluate the effectiveness of our network on the latest VVC Test Model VTM. The experimental results demonstrate that our proposed method achieves on average 9.7% bit saving compared with VVC under low-delay P configuration.

Qian Liu, Ran Wei, Jian Lu, Hongke Ding, Hui Yi, Li Guo, Jing Wu Department of Medical Genetics Center, Guangdong Women and Children Hospital, Guangzhou City, Guangdong Province, People’s Republic of ChinaCorrespondence: Jing Wu, Department of Medical Genetics Center, Guangdong Women and Children Hospital, NO. 521 Xingnan Road, Panyu District, Guangzhou city, Guangdong Province, People’s Republic of China, Tel +86 20-39151548, Email wujing020@126.comObjective: To analyse the risk of clinical chromosomal abnormalities in foetuses with umbilical cord cysts.Methods: Data from all genetic assays that were performed as part of invasive prenatal diagnoses of umbilical cord cysts between October 2014 and June 2021 were retrospectively collected from Guangdong Women and Children Hospital. We compared the differences in genetic assay findings in isolated and nonisolated umbilical cord cyst cohorts.Results: A total of 49 singleton pregnancies and 2 foetuses that were one of the cotwins in monochorionic twin pregnancies were enrolled in the cohort; 20 isolated and 31 nonisolated umbilical cord cysts were identified in the cohort. One foetus (5%, 1/20) in the isolated umbilical cord cyst group showed chromosomal abnormalities and 17p12 microduplication. Twelve cases (38.7%, 12/31) of chromosomal abnormalities, including seven cases of trisomy 18, two cases of trisomy 13 and three cases of microdeletion, were identified in the nonisolated umbilical cord cyst group. The incidences of chromosomal abnormalities between the two groups were significantly different (1/20, 5% vs 13/31, 38.7%, p=0.003). There was no relative pathological medical exome sequencing finding in the three foetuses suffering from nonisolated umbilical cord cysts whose parents chose to undergo chromosomal microarray analysis (CMA) and medical exome sequencing.Conclusion: This retrospective cohort study evaluated the value of CMA in foetuses with umbilical cord cysts and suggested that copy n

Pancreatic cancer is a complex disease, in need of new therapeutic approaches. In this study, we explored the effect and mechanism of action of epigallocatechin-3-gallate (EGCG), a major polyphenol in green tea, alone and in combination with current chemotherapeutics on pancreatic cancer cell growth, focusing on glycolysis metabolism. Moreover, we investigated whether EGCG's effect is dependent on its ability to induce reactive oxygen species (ROS). EGCG reduced pancreatic cancer cell growth in a concentration-dependent manner and the growth inhibition effect was further enhanced under glucose deprivation conditions. Mechanistically, EGCG induced ROS levels concentration-dependently. EGCG affected glycolysis by suppressing the extracellular acidification rate through the reduction of the activity and levels of the glycolytic enzymes phosphofructokinase and pyruvate kinase. Cotreatment with catalase abrogated EGCG's effect on phosphofructokinase and pyruvate kinase. Furthermore, EGCG sensitized gemcitabine to inhibit pancreatic cancer cell growth in vitro and in vivo. EGCG and gemcitabine, given alone, reduced pancreatic tumor xenograft growth by 40% and 52%, respectively, whereas the EGCG/gemcitabine combination reduced tumor growth by 67%. EGCG enhanced gemcitabine's effect on apoptosis, cell proliferation, cell cycle and further suppressed phosphofructokinase and pyruvate kinase levels. In conclusion, EGCG is a strong combination partner of gemcitabine reducing pancreatic cancer cell growth by suppressing glycolysis.

Pancreatic cancer is a complex disease, in need of new therapeutic approaches. In this study, we explored the effect and mechanism of action of epigallocatechin-3-gallate (EGCG), a major polyphenol in green tea, alone and in combination with current chemotherapeutics on pancreatic cancer cell growth, focusing on glycolysis metabolism. Moreover, we investigated whether EGCG's effect is dependent on its ability to induce reactive oxygen species (ROS). EGCG reduced pancreatic cancer cell growth in a concentration-dependent manner and the growth inhibition effect was further enhanced under glucose deprivation conditions. Mechanistically, EGCG induced ROS levels concentration-dependently. EGCG affected glycolysis by suppressing the extracellular acidification rate through the reduction of the activity and levels of the glycolytic enzymes phosphofructokinase and pyruvate kinase. Cotreatment with catalase abrogated EGCG's effect on phosphofructokinase and pyruvate kinase. Furthermore, EGCG sensitized gemcitabine to inhibit pancreatic cancer cell growth in vitro and in vivo. EGCG and gemcitabine, given alone, reduced pancreatic tumor xenograft growth by 40% and 52%, respectively, whereas the EGCG/gemcitabine combination reduced tumor growth by 67%. EGCG enhanced gemcitabine's effect on apoptosis, cell proliferation, cell cycle and further suppressed phosphofructokinase and pyruvate kinase levels. In conclusion, EGCG is a strong combination partner of gemcitabine reducing pancreatic cancer cell growth by suppressing glycolysis.

Robust visual localization for urban vehicles remains challenging and unsolved. The limitation of computation efficiency and memory size has made it harder for large-scale applications. Since semantic information serves as a stable and compact representation of the environment, we propose a coarse-to-fine localization system based on a semantic compact map. Pole-like objects are stored in the compact map, then are extracted from semantically segmented images as observations. Localization is performed by a particle filter, followed by a pose alignment module decoupling translation and rotation to achieve better accuracy. We evaluate our system both on synthetic and realistic datasets and compare it with two baselines, a state-of-Art semantic feature-based system, and a traditional SIFT feature-based system. Experiments demonstrate that even with a significantly small map, such as a 10 KB map for a 3.7 km long trajectory, our system provides a comparable accuracy with the baselines. A video demo: https://youtu.be/XbTc1YNPajc.

The Xiaoqinling gold field, located along the southern margin of the North China craton, is the second largest gold producer in China, which comprises more than 1 200 auriferous quartz veins with a proven gold reserve of at least 800 tons. Previously, the absolute age of the gold metallogenesis in this area has not been well defined due to the lack of suitable dating minerals. This study presents new in situ laser ablation-inductively coupled plasma-mass spectrometry U-Pb ages of coexisting hydrothermal monazite and rutile for the Fancha gold deposit in this area, which yielded 206Pb/238U ages of 127.5 ± 0.7 Ma (n=65, mean square of weighted deviates [MSWD]=1.8) and 129.7 ± 4.3 Ma (n=37, MSWD=1.4), respectively. Both ages overlap within analytical uncertainty at the 2sigma level of significance, suggesting that both gold-bearing veins were emplaced at ca. 128 Ma. Mineralogical observations indicate that the monazite and rutile precipitated simultaneously with gold from the hydrothermal fluid. Our new data, combined with recently published monazite age, define a more precise gold episode, demonstrating that the gold endowment of the Xiaoqinling area was formed during a relatively brief period at ca. 130 to 127 Ma. It is suggested that auriferous fluids were generated as a result of interactions between the enriched mantle and the lower crust, which was driven by westward flat slab subduction of the Palaeo-Pacific plate during the late Mesozoic. The peak of lithospheric thinning during the postsubduction may have led to the rapid release of gold from the fertilised mantle. Consequently, the large number of gold-bearing veins in the Xiaoqinling area may ultimately be related to the tectonic evolution and mantle fluid processes that occurred during Early Cretaceous lithospheric extension. (Authors.), The Xiaoqinling gold field, located along the southern margin of the North China craton, is the second largest gold producer in China, which comprises more than 1 200 auriferous quartz veins with a proven gold reserve of at least 800 tons. Previously, the absolute age of the gold metallogenesis in this area has not been well defined due to the lack of suitable dating minerals. This study presents new in situ laser ablation-inductively coupled plasma-mass spectrometry U-Pb ages of coexisting hydrothermal monazite and rutile for the Fancha gold deposit in this area, which yielded 206Pb/238U ages of 127.5 ± 0.7 Ma (n=65, mean square of weighted deviates [MSWD]=1.8) and 129.7 ± 4.3 Ma (n=37, MSWD=1.4), respectively. Both ages overlap within analytical uncertainty at the 2sigma level of significance, suggesting that both gold-bearing veins were emplaced at ca. 128 Ma. Mineralogical observations indicate that the monazite and rutile precipitated simultaneously with gold from the hydrothermal fluid. Our new data, combined with recently published monazite age, define a more precise gold episode, demonstrating that the gold endowment of the Xiaoqinling area was formed during a relatively brief period at ca. 130 to 127 Ma. It is suggested that auriferous fluids were generated as a result of interactions between the enriched mantle and the lower crust, which was driven by westward flat slab subduction of the Palaeo-Pacific plate during the late Mesozoic. The peak of lithospheric thinning during the postsubduction may have led to the rapid release of gold from the fertilised mantle. Consequently, the large number of gold-bearing veins in the Xiaoqinling area may ultimately be related to the tectonic evolution and mantle fluid processes that occurred during Early Cretaceous lithospheric extension. (Authors.)

Robust visual localization for urban vehicles remains challenging and unsolved. The limitation of computation efficiency and memory size has made it harder for large-scale applications. Since semantic information serves as a stable and compact representation of the environment, we propose a coarse-to-fine localization system based on a semantic compact map. Pole-like objects are stored in the compact map, then are extracted from semantically segmented images as observations. Localization is performed by a particle filter, followed by a pose alignment module decoupling translation and rotation to achieve better accuracy. We evaluate our system both on synthetic and realistic datasets and compare it with two baselines, a state-of-Art semantic feature-based system, and a traditional SIFT feature-based system. Experiments demonstrate that even with a significantly small map, such as a 10 KB map for a 3.7 km long trajectory, our system provides a comparable accuracy with the baselines. A video demo: https://youtu.be/XbTc1YNPajc.

Automated vehicle (AV) technologies may significantly improve driving safety, but only if they are widely adopted and used appropriately. Adoption and appropriate use are influenced by user expectations, which are increasingly being driven by social media. In the context of AVs, prior studies have observed that major news events such as crashes and technology announcements influence user responses to AVs; however, the exact impact and dynamics of this influence are not well understood. The goals of this project were to develop a novel search method to identify AV-relevant user comments on Twitter, mine these tweets to understand the influence of crashes and news events on user sentiment about AVs, and finally translate these findings into a set of guidelines for reporting about AV crashes. In service of these goals, we developed a novel semi-supervised constrained-level learning machine search approach to identify relevant tweets and demonstrated that it outperformed alternative methods. We used the relevant tweets identified to develop a topic model of AV events which illustrated that crashes, fault and safety, and technology companies were the most discussed topics following major events. While the sentiment among these topics was mostly neutral, tweets about crashes and fault and safety were negatively biased. We combined these findings with a series of interviews with Public Information Officers to develop a set of five basic guidelines for AV communication. These guidelines should aid proper public calibration and subsequent acceptance and use of AVs.

T-lymphokine-activated killer (T-LAK) cell-originated protein kinase (TOPK) is an emerging target with critical roles in various cancers; however, its expression and function in osteosarcoma remain unexplored. We evaluated TOPK expression using RNA sequencing and gene expression data from public databases (TARGET-OS, CCLE, GTEx, and GENT2) and immunohistochemistry in an osteosarcoma tissue microarray (TMA). TOPK gene expression was significantly higher in osteosarcoma than normal tissues and directly correlated with shorter overall survival. TOPK was overexpressed in 83.3% of the osteosarcoma specimens within our TMA and all osteosarcoma cell lines, whereas normal osteoblast cells had no aberrant expression. High expression of TOPK associated with metastasis, disease status, and shorter overall survival. Silencing of TOPK with small interfering RNA (siRNA) decreased cell viability, and inhibition with the selective inhibitor OTS514 suppressed osteosarcoma cell proliferation, migration, colony-forming ability, and spheroid growth. Enhanced chemotherapeutic sensitivity and a synergistic effect were also observed with the combination of OTS514 and either doxorubicin or cisplatin in osteosarcoma cell lines. Taken together, our study demonstrated that TOPK is a potential prognostic biomarker and therapeutic target for osteosarcoma treatment.

Automated vehicle (AV) technologies may significantly improve driving safety, but only if they are widely adopted and used appropriately. Adoption and appropriate use are influenced by user expectations, which are increasingly being driven by social media. In the context of AVs, prior studies have observed that major news events such as crashes and technology announcements influence user responses to AVs; however, the exact impact and dynamics of this influence are not well understood. The goals of this project were to develop a novel search method to identify AV-relevant user comments on Twitter, mine these tweets to understand the influence of crashes and news events on user sentiment about AVs, and finally translate these findings into a set of guidelines for reporting about AV crashes. In service of these goals, we developed a novel semi-supervised constrained-level learning machine search approach to identify relevant tweets and demonstrated that it outperformed alternative methods. We used the relevant tweets identified to develop a topic model of AV events which illustrated that crashes, fault and safety, and technology companies were the most discussed topics following major events. While the sentiment among these topics was mostly neutral, tweets about crashes and fault and safety were negatively biased. We combined these findings with a series of interviews with Public Information Officers to develop a set of five basic guidelines for AV communication. These guidelines should aid proper public calibration and subsequent acceptance and use of AVs.

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article.

BackgroundIn the intent-to-treat (ITT) population of the RAINBOW study, objective response rate (ORR) was 28% and 16% in the ramucirumab and control arms, respectively. To further characterize tumor response, we present details on timing and extent of tumor shrinkage, as well as associations with symptom palliation.Materials and methodsTumor response was assessed with RECIST v1.1, and quality of life (QoL) was assessed with the European Organization for Research and Treatment of Cancer Quality-of-Life Questionnaire-Core 30 (EORTC QLQ-C30) v3.0. Prespecified and post hoc analyses were conducted in the ITT population, patients with measurable disease, or responders, and included best overall response (BOR), ORR, disease control rate (DCR), duration of response, time to response (TtR), change in tumor size, and associations of QoL with tumor shrinkage and BOR.ResultsIn both treatment arms, median TtR was 1.5 months. Responses were more durable in the ramucirumab versus control arm (median 4.4 vs. 2.8 months). In patients with measurable disease (78% of ITT), ORR was 36% versus 20%; DCR was 81% versus 61% in the ramucirumab versus control arms. Waterfall plots demonstrated more tumor shrinkage in the ramucirumab versus control arm. Regardless of treatment, tumor response and stable disease were associated with improved or stable QoL, with more tumor shrinkage associated with greater symptom palliation.ConclusionTreatment with ramucirumab plus paclitaxel yielded the highest ORR reported to date for patients with previously treated advanced gastric or gastroesophageal junction adenocarcinoma. Additional details demonstrate robustness of tumor response results. The extent of tumor shrinkage is directly associated with symptom palliation and should be considered when evaluating patient needs and treatment selection. Clinical trial identification number. NCT01170663.Implications for practiceRamucirumab plus paclitaxel is a recognized standard of care as it improves survival

Automated vehicle (AV) technologies may significantly improve driving safety, but only if they are widely adopted and used appropriately. Adoption and appropriate use are influenced by user expectations, which are increasingly being driven by social media. In the context of AVs, prior studies have observed that major news events such as crashes and technology announcements influence user responses to AVs; however, the exact impact and dynamics of this influence are not well understood. The goals of this project were to develop a novel search method to identify AV-relevant user comments on Twitter, mine these tweets to understand the influence of crashes and news events on user sentiment about AVs, and finally translate these findings into a set of guidelines for reporting about AV crashes. In service of these goals, we developed a novel semi-supervised constrained-level learning machine search approach to identify relevant tweets and demonstrated that it outperformed alternative methods. We used the relevant tweets identified to develop a topic model of AV events which illustrated that crashes, fault and safety, and technology companies were the most discussed topics following major events. While the sentiment among these topics was mostly neutral, tweets about crashes and fault and safety were negatively biased. We combined these findings with a series of interviews with Public Information Officers to develop a set of five basic guidelines for AV communication. These guidelines should aid proper public calibration and subsequent acceptance and use of AVs.

Robust visual localization for urban vehicles remains challenging and unsolved. The limitation of computation efficiency and memory size has made it harder for large-scale applications. Since semantic information serves as a stable and compact representation of the environment, we propose a coarse-to-fine localization system based on a semantic compact map. Pole-like objects are stored in the compact map, then are extracted from semantically segmented images as observations. Localization is performed by a particle filter, followed by a pose alignment module decoupling translation and rotation to achieve better accuracy. We evaluate our system both on synthetic and realistic datasets and compare it with two baselines, a state-of-Art semantic feature-based system, and a traditional SIFT feature-based system. Experiments demonstrate that even with a significantly small map, such as a 10 KB map for a 3.7 km long trajectory, our system provides a comparable accuracy with the baselines. A video demo: https://youtu.be/XbTc1YNPajc.

Automated vehicle (AV) technologies may significantly improve driving safety, but only if they are widely adopted and used appropriately. Adoption and appropriate use are influenced by user expectations, which are increasingly being driven by social media. In the context of AVs, prior studies have observed that major news events such as crashes and technology announcements influence user responses to AVs; however, the exact impact and dynamics of this influence are not well understood. The goals of this project were to develop a novel search method to identify AV-relevant user comments on Twitter, mine these tweets to understand the influence of crashes and news events on user sentiment about AVs, and finally translate these findings into a set of guidelines for reporting about AV crashes. In service of these goals, we developed a novel semi-supervised constrained-level learning machine search approach to identify relevant tweets and demonstrated that it outperformed alternative methods. We used the relevant tweets identified to develop a topic model of AV events which illustrated that crashes, fault and safety, and technology companies were the most discussed topics following major events. While the sentiment among these topics was mostly neutral, tweets about crashes and fault and safety were negatively biased. We combined these findings with a series of interviews with Public Information Officers to develop a set of five basic guidelines for AV communication. These guidelines should aid proper public calibration and subsequent acceptance and use of AVs.

Underwater image restoration is of significant importance in unveiling the underwater world. Numerous techniques and algorithms have been developed in the past decades. However, due to fundamental difficulties associated with imaging/sensing, lighting, and refractive geometric distortions, in capturing clear underwater images, no comprehensive evaluations have been conducted of underwater image restoration. To address this gap, we have constructed a large-scale real underwater image dataset, dubbed `HICRD' (Heron Island Coral Reef Dataset), for the purpose of benchmarking existing methods and supporting the development of new deep-learning based methods. We employ accurate water parameter (diffuse attenuation coefficient) in generating reference images. There are 2000 reference restored images and 6003 original underwater images in the unpaired training set. Further, we present a novel method for underwater image restoration based on unsupervised image-to-image translation framework. Our proposed method leveraged contrastive learning and generative adversarial networks to maximize the mutual information between raw and restored images. Extensive experiments with comparisons to recent approaches further demonstrate the superiority of our proposed method. Our code and dataset are publicly available at GitHub., Comment: In submission, code/dataset are at https://github.com/JunlinHan/CWR. arXiv admin note: text overlap with arXiv:2103.09697

Underwater image restoration attracts significant attention due to its importance in unveiling the underwater world. This paper elaborates on a novel method that achieves state-of-the-art results for underwater image restoration based on the unsupervised image-to-image translation framework. We design our method by leveraging from contrastive learning and generative adversarial networks to maximize mutual information between raw and restored images. Additionally, we release a large-scale real underwater image dataset to support both paired and unpaired training modules. Extensive experiments with comparisons to recent approaches further demonstrate the superiority of our proposed method., Comment: Accepted to IGARSS 2021 as oral presentation. Code is available at https://github.com/JunlinHan/CWR

Chunxiang Li,1 Hengchang Liu,2 Ran Wei,2 Zheng Liu,2 Haipeng Chen,2 Xu Guan,2 Zhixun Zhao,2 Xishan Wang,2 Zheng Jiang2 1Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China; 2Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of ChinaCorrespondence: Zheng JiangDepartment of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing, People’s Republic of ChinaTel +86 10-87787110Email zr7780@163.comBackground/Aims: Long non-coding ribonucleic acids (lncRNAs) are involved in the progression of cancers and affect the response to radiation therapy. This study was to investigate the mechanism of lncRNA EGOT in the radiosensitivity of rectal cancer.Methods: The mRNA expression of EGOT, miR-211-5p and ErbB4 in rectal cancer tissues and cells was detected by qRT-PCR. The protein expression of ErbB4 was detected by Western blot. Dual-luciferase reporter assay and ribonucleic acid immunoprecipitation (RIP) were used to confirm the interaction between EGOT and miR-211-5p or miR-211-5p and ErbB4. Transfection technology was used to down-regulate and up-regulate the expression of EGOT and miR-211-5p in rectal cancer cells, respectively. MTT, colony formation and flow cytometry were used to detect the effect of EGOT and miR-211-5p on proliferation, invasion, migration and apoptosis of rectal cancer cells.Results: The expression of EGOT was up-regulated in rectal cancer tissues and cells, and the expression of EGOT was related to the late stage of pathology. EGOT knockdown inhibited

New chemotherapeutic agents are needed for pancreatic cancer (PC). We have previously shown that phospho-valproic acid (MDC-1112) is effective in cell-line xenografts of PC. Here, we explored whether MDC-1112 is effective in additional clinically relevant animal models of PC and whether MDC-1112 enhances the anticancer effect of clinically used chemotherapeutic agents. MDC-1112 alone strongly reduced patient-derived pancreatic tumor xenograft growth, and extended survival of LSL-KrasG12D/+; LSL-Trp53R172H/+; Pdx1-Cre (KPC) mice. In both models, MDC-1112 inhibited STAT3 activation and its downstream signals, including Bcl-xL and cyclin D1. In human PC cell lines, P-V enhanced the growth inhibitory effect of gemcitabine (GEM), Abraxane and 5-FU, but not that of irinotecan. Normal human pancreatic epithelial cells were more resistant to the cytotoxic effects of MDC-1112/GEM combination. Furthermore, MDC-1112 enhanced GEM's effect on colony formation, apoptosis, cell migration, and cell invasion. In vivo, MDC-1112 and GEM, given alone, reduced patient-derived pancreatic tumor xenograft growth by 58% and 87%, respectively; whereas MDC-1112/GEM combination reduced tumor growth by 94%, inducing tumor stasis. In conclusion, MDC-1112 should be further explored as a potential agent to be used in combination with GEM for treating PC.

New chemotherapeutic agents are needed for pancreatic cancer (PC). We have previously shown that phospho-valproic acid (MDC-1112) is effective in cell-line xenografts of PC. Here, we explored whether MDC-1112 is effective in additional clinically relevant animal models of PC and whether MDC-1112 enhances the anticancer effect of clinically used chemotherapeutic agents. MDC-1112 alone strongly reduced patient-derived pancreatic tumor xenograft growth, and extended survival of LSL-KrasG12D/+; LSL-Trp53R172H/+; Pdx1-Cre (KPC) mice. In both models, MDC-1112 inhibited STAT3 activation and its downstream signals, including Bcl-xL and cyclin D1. In human PC cell lines, P-V enhanced the growth inhibitory effect of gemcitabine (GEM), Abraxane and 5-FU, but not that of irinotecan. Normal human pancreatic epithelial cells were more resistant to the cytotoxic effects of MDC-1112/GEM combination. Furthermore, MDC-1112 enhanced GEM's effect on colony formation, apoptosis, cell migration, and cell invasion. In vivo, MDC-1112 and GEM, given alone, reduced patient-derived pancreatic tumor xenograft growth by 58% and 87%, respectively; whereas MDC-1112/GEM combination reduced tumor growth by 94%, inducing tumor stasis. In conclusion, MDC-1112 should be further explored as a potential agent to be used in combination with GEM for treating PC.

TianQin is a planned space-based gravitational wave (GW) observatory consisting of three Earth-orbiting satellites with an orbital radius of about $10^5 \, {\rm km}$. The satellites will form an equilateral triangle constellation the plane of which is nearly perpendicular to the ecliptic plane. TianQin aims to detect GWs between $10^{-4} \, {\rm Hz}$ and $1 \, {\rm Hz}$ that can be generated by a wide variety of important astrophysical and cosmological sources, including the inspiral of Galactic ultra-compact binaries, the inspiral of stellar-mass black hole binaries, extreme mass ratio inspirals, the merger of massive black hole binaries, and possibly the energetic processes in the very early universe and exotic sources such as cosmic strings. In order to start science operations around 2035, a roadmap called the 0123 plan is being used to bring the key technologies of TianQin to maturity, supported by the construction of a series of research facilities on the ground. Two major projects of the 0123 plan are being carried out. In this process, the team has created a new-generation $17 \, {\rm cm}$ single-body hollow corner-cube retro-reflector which was launched with the QueQiao satellite on 21 May 2018; a new laser-ranging station equipped with a $1.2 \, {\rm m}$ telescope has been constructed and the station has successfully ranged to all five retro-reflectors on the Moon; and the TianQin-1 experimental satellite was launched on 20 December 2019 - the first-round result shows that the satellite has exceeded all of its mission requirements.