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Multimodal analgesic strategy is pivotal for enhanced recovery after surgery. The objective of this trial was to assess the effect of subanesthetic esketamine vs. placebo combined with erector spinae plane block (ESPB) vs. intercostal nerve block (ICNB) on postoperative recovery following thoracoscopic lung resection. This randomized, controlled, 2×2 factorial trial was conducted at a university hospital in Suzhou, China. One hundred adult patients undergoing thoracoscopic lung surgery were randomized to one of four groups (esketamine-ESPB, esketamine-ICNB, placebo-ESPB, and placebo-ICNB) to receive i.v. esketamine 0.3 mg/kg or normal saline placebo combined with ESPB or ICNB using 0.375% ropivacaine 20 mL. All patients received flurbiprofen axetil and patient-controlled fentanyl. The primary outcome was quality of recovery (QoR) at 24 h postoperatively, assessed using the QoR-15 scale, with a minimal clinically important difference of 6.0. The median age was 57 years and 52% were female. No significant interaction effect was found between esketamine and regional blocks on QoR (P=0.215). The QoR-15 score at 24 h was 111.5±5.8 in the esketamine group vs. 105.4±4.5 in the placebo group (difference=6.1, 95% CI, 4.0-8.1; P<0.001); 109.7±6.2 in the ESPB group vs. 107.2±5.6 in the ICNB group (difference=2.5, 95% CI, 0.2-4.9; P=0.033; not statistically significant after Bonferroni correction). Additionally, esketamine resulted in higher QoR-15 scores at 48 h (difference=4.6) and hospital discharge (difference=1.6), while ESPB led to a higher QoR-15 score at 48 h (difference=3.0). For patients undergoing thoracoscopic lung resection, subanesthetic esketamine improved QoR after surgery, while ICNB can be used interchangeably with ESPB as a component of multimodal analgesia.

Mutations in the Cockayne Syndrome group B (CSB) gene cause cancer in mice, but premature aging and severe neurodevelopmental defects in humans. CSB, a member of the SWI/SNF family of chromatin remodelers, plays diverse roles in regulating gene expression and transcription-coupled nucleotide excision repair (TC-NER); however, these functions do not explain the distinct phenotypic differences observed between CSB-deficient mice and humans. During investigating Cockayne Syndrome-associated genome instability, we uncover an intrinsic mechanism that involves elongating RNA polymerase II (RNAPII) undergoing transient pauses at internal T-runs where CSB is required to propel RNAPII forward. Consequently, CSB deficiency retards RNAPII elongation in these regions, and when coupled with G-rich sequences upstream, exacerbates genome instability by promoting R-loop formation. These R-loop prone motifs are notably abundant in relatively long genes related to neuronal functions in the human genome, but less prevalent in the mouse genome. These findings provide mechanistic insights into differential impacts of CSB deficiency on mice versus humans and suggest that the manifestation of the Cockayne Syndrome phenotype in humans results from the progressive evolution of mammalian genomes.

Liu R, Chen Y, Shou T, Hu J, Qing C. Onco Targets Ther. 2019;12:2615–2621. We, the Editor and Publisher of the journal OncoTargets and Therapy, have retracted the following article. Following publication of the article, concerns were raised about the duplication of images from Figures 2 and 4 with images from unrelated articles. Specifically, Images for Figure 2C and 2D have been duplicated with images for Figure 2d and 2e from Jiang N, Jiang X, Chen Z, et al. MiR-203a-3p suppresses cell proliferation and metastasis through inhibiting LASP1 in nasopharyngeal carcinoma. J Exp Clin Cancer Res. 2017;36:138. https://doi.org/10.1186/s13046-017-0604-3 and Figure 3C from Liu B, Li G, Zhang Z, Wu H. RETRACTED: Influence of miR-376c-3p/SYF2 Axis on the Progression of Gastric Cancer. Technology in Cancer Research & Treatment. 2019;18. doi:10.1177/1533033819874808. Images from Figure 4D have been duplicated with images for Figures 2e and 3e from Jiang N, et al (2017). The authors responded to our queries but were unable to provide a satisfactory explanation for the duplicated images or provide satisfactory data for the study. As verifying the validity of published work is core to the integrity of the scholarly record, the Publisher and Editor requested to retract the article and the authors agree with this. We have been informed in our decision-making by our editorial policies and COPE guidelines. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.

Yanjuan Song,1 Jing Hu,2 Chunlian Ma,3– 5 Hua Liu,3– 5 Zhanghua Li,6 Yi Yang3– 5 1Graduate School, Wuhan Sports University, Wuhan, Hubei Province, People’s Republic of China; 2Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, Hubei Province, People’s Republic of China; 3Fitness Monitoring and Chronic Disease Intervention Research Center, Wuhan Sports University, Wuhan, Hubei Province, People’s Republic of China; 4College of Sports Medicine, Wuhan Sports University, Wuhan, Hubei Province, People’s Republic of China; 5Hubei Key Laboratory of Exercise Training and Monitoring, Wuhan Sports University, Wuhan, Hubei Province, People’s Republic of China; 6Department of Orthopedics, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, Hubei Province, People’s Republic of ChinaCorrespondence: Yi Yang, Fitness Monitoring and Chronic Disease Intervention research center, Wuhan Sports University, Wuhan, Hubei Province, 430079, People’s Republic of China, Tel +86 139 7129 3990, Email yangyi999999@foxmail.com Zhanghua Li, Department of Orthopedics, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, Hubei Province, 430074, People’s Republic of China, Tel +86 189 7161 0121, Email lizhanghua_123@163.comAbstract: The development of numerous diseases is significantly influenced by inflammation. Macrophage-derived exosomes (M-Exos) play a role in controlling inflammatory reactions in various conditions, including chronic inflammatory pain, hypertension, and diabetes. However, the specific targets and roles of M-Exos in regulating inflammation in diseases remain largely unknown. This review summarizes current knowledge on M-Exos biogenesis and provides updated information on M-Exos’ biological function in inflammation modulation. Furthermore, this review highlights the functionalization and engineering strategies of M-Exos, while providing an overview of cutting-edge approach

Jifeng Li,1 Dongxiao Zhang,1 Jing Hu,1 Jianchun Cui,2 Khattak Mazher Mansoor2 1Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China; 2Liaoning Provincial People’s Hospital (Department of Thyroid and Breast Surgery, People’s Hospital of China Medical University), Shenyang, People’s Republic of ChinaCorrespondence: Dongxiao Zhang, Department of Galactophore, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, No. 23, Art Museum Back Street, Dongcheng District, Beijing, 100010, People’s Republic of China, Tel +8617864190977, Email morningdong@163.comObjective: This study aims to systematically evaluate the effectiveness and safety of acupuncture in treating mammary hyperplasia.Methods: A comprehensive search was conducted in various databases, including PubMed, Web of Science, Cochrane Library, Embase, SinoMed, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP Database, from their inception until July 2023. Only randomized controlled trials (RCTs) researching the use of acupuncture for mammary hyperplasia were included. Quality assessment and data analysis were performed using RevMan 5.3 software.Results: Eight RCTs, comprising 573 patients, were included in this analysis. The meta-analysis revealed that in the acupuncture group, the experimental group was better than the control group in improving breast pain, breast lump extent, and the texture of breast lump (P=0.0007, I2=61%; P=0.02, I2=0%; P=0.0002, I2=0%). We found that both could be improved, but after statistical analysis, there was no significant advantage in the acupuncture group compared with the patent Chinese medicine group (p> 0.05).Conclusion: Acupuncture appears to be an effective and safe treatment for mammary hyperplasia, providing pain relief and reducing mass volume, texture, and extent. However, the insufficient quality of the available evidence indicates the need for further methodolo

This paper reports an enhanced approach of thermal reduction of phosphogypsum (PG) in the presence of sodium chloride (NaCl) in its molten phase. The thermodynamic together with in-situ thermogravimetric-Fourier transform infrared spectroscopy (TG-FTIR) result indicates the yield of CaS (solid) at the investigated temperature range (800–850 °C). Addition of NaCl not only appreciably reduces the activation energy of this reaction (800–850 °C) on average from 315 to 175 (kJ·mol−1) materially, but also significantly improve the conversion from CaSO4 to CaS. Additionally, the NaCl based waste salt (NaCl-WS) containing organic compounds (0.21 wt%) was found to produce similar results when compared with the pure NaCl addition at investigated temperature range. The optimal conditions via parametric optimization: i.e., 800–850 °C, 15 wt% (NaCl-WS), and 6 (ratio of PG/C (carbon) wt/wt), were determined. The mechanistic study together with using density functional theories (DFT) shows that the synergistic effect by NaCl addition was caused by the coupled-vacancy diffusion mechanism when NaCl melts at high temperature and C atom shows proximity to oxygen (O) atom from the SO42- cluster in the CaSO4 crystallite structure. The evaluation of the process shows appealing advantages over the conventional PG thermal reduction. By carefully manipulating temperature range together with the NaCl-WS addition, the proposed new process simultaneously realizes PG thermal reduction and environmental utilization of NaCl-WS.

This review focuses on the parametric impacts upon conversion and selectivity during CO2 hydrogenation via Fischer-Tropsch (FT) synthesis using iron-based catalyst to provide quantitative evaluation. Using all collected data from reported literatures as training dataset via artificial neural networks (ANNs) in TensorFlow, three categorized parameters (namely: operational, catalyst informatic and mass transfer) were deployed to assess their impacts upon conversions (CO2) and selectivity. The lump kinetic power expressions among literature reports were compared, and the best fit model is the one that was proposed by this work without arbitrarily assuming power values of individual partial pressure (CO and H2). More than five sets of binary parameters were systematically investigated to find out corresponding evolving patterns in conversion and selectivity. Aided by machine learning, tailoring product distributions based on specific selectivity or conversion for optimization purpose is practically achievable by deploying the predictions generated from ANNs in this work.

Xuan Wang,1,&ast; Jing-Xian Hu,2,&ast; Ying Wang,1 Fang Wang,1 Xuan Wu,1 Fei Wang,1 Yan-Chao Zhao,1 Xiao-Lan Wang,2 Li-Li Zhang2 1Second Department of Hepatology Center for Liver Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People’s Republic of China; 2Department of Nursing, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Li-Li Zhang; Jing-Xian Hu, Department of Nursing, Beijing Youan Hospital, Capital Medical University, No. 8 of Xitoutiao, Youanmen wai, FengtaiDistrict, Beijing, 100069, People’s Republic of China, Email zhanglyliy@163.com; 819211326@mail.ccmu.edu.cnObjective: This study explores the correlation between coping style, quality of life, and illness uncertainty in the family caregivers of patients with liver cancer.Methods: Employing convenience sampling, 210 family caregivers of patients with liver cancer who met the admission criteria were selected from a grade A infectious disease hospital in Beijing between January and December 2022. A cross-sectional survey was conducted using the Simplified Coping Style Questionnaire, Caregiver Quality of Life, and the Mishel Uncertainty in Illness Scale for Family Members. This study analysed the correlations between coping styles, quality of life, and illness uncertainty in these caregivers.Results: The study found that family caregivers of patients with liver cancer had average scores for illness uncertainty (83.44 ± 11.86), coping style (33.19 ± 9.79; both positive [23.02 ± 6.81] and negative [10.17 ± 5.05]), and quality of life (169.53 ± 32.46). A negative association was observed between illness uncertainty in these caregivers and positive coping style (r = − 0.207, p = 0.003), physical status (r = − 0.182, p = 0.008), psychological status (r = − 0.200, p = 0.004), and social adaptation (r = − 0.229, p = 0.001).Conclusion: The stud

Jing-Xian Hu,1,&ast; Dong-Xia Wu,2,&ast; Xuan Wang,3,&ast; Yu-Ling Zhang,4 Hui-Min Guo,1 Jian-Hong Ma,2 Yun Zhang,2 Xiao-Jing Lian,5 Xin Wang,1 Lili Zhang1 1Department of Nursing, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People’s Republic of China; 2Department of Infection, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People’s Republic of China; 3Second Department of Hepatology Center for Liver Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People’s Republic of China; 4Department of Anesthesiology and Surgery, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People’s Republic of China; 5Integrated Traditional Chinese and Western Medicine Center, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Lili Zhang; Xin Wang, Department of Nursing, Beijing Youan Hospital, Capital Medical University, No. 8, Xi Tou Tiao, Youanmen Wai, Fengtai District, Beijing, 100069, People’s Republic of China, Tel/Fax +86 10 8399 7143, Email zhanglyliy@163.com; 13521101275@mail.ccmu.edu.cnObjective: To analyze the relevant research publications on infectious disease nursing in China to understand the current research status of infectious disease in nursing.Methods: Retrieve relevant literature on infectious disease in nursing from the establishment of the Chinese Biomedical Literature Database, China National Knowledge Infrastructure (CNKI), VIP Database, and Wanfang Database until May 10, 2021. Conduct bibliometric analysis using CiteSpace software. Key words were analyzed using cluster analysis.Results: A total of 4693 relevant literature on infectious disease research in nursing care were included in this study. The overall number of publications on infectious disease research in nursing showed an increasing trend, with a peak in 2010. There were 32

Wen Wen,1,&ast; Ping Lei,1,&ast; Wantai Dang,2 Liwen Ma,1 Jing Hu,1 Jian Liu1 1Department of Ultrasound, First Affiliated Hospital, Clinical Medical College of Chengdu Medical College, Chengdu, People’s Republic of China; 2Department of Rheumatology, First Affiliated Hospital, Clinical Medical College of Chengdu Medical College, Chengdu, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jian Liu, Department of Ultrasound, First Affiliated Hospital of Chengdu Medical College, 278 Baoguang Avenue, Xindu District, Chengdu, Si Chuan, 610500, People’s Republic of China, Email liujiansh@126.comObjective: This study aimed to employ echocardiography for measuring the markers of left ventricular (LV) diastolic function to investigate the effects of family history of gout on the LV diastolic function in patients with primary gout.Methods: Two hundred and eighty-four patients with primary gout who visited the Department of Rheumatology and Immunology of the First Affiliated Hospital of Chengdu Medical College from September 2020 to July 2022 were selected and their family history of gout, general information, and laboratory markers were recorded. Parameters of LV diastolic function were measured via echocardiography. The correlation between family history and LV diastolic function markers was analyzed using univariate and multivariate regression and the receiver operating characteristic (ROC) curve analyses.Results: LV diastolic function parameters, peak early mitral diastolic velocity (E)/peak late mitral diastolic velocity (A), and early septal mitral annulus diastolic motion velocity (Sepe’), early lateral mitral annulus diastolic motion velocity (Late’) and their mean (e’), were significantly lower in patients with familial primary gout, while left atrial volume index (LAVI) and E/e’ were markedly elevated in patients with sporadic primary gout. In patients with family history, the proportion of grade ≥

An important development direction in the Single-Image Super-Resolution (SISR) algorithms is to improve the efficiency of the algorithms. Recently, efficient Super-Resolution (SR) research focuses on reducing model complexity and improving efficiency through improved deep small kernel convolution, leading to a small receptive field. The large receptive field obtained by large kernel convolution can significantly improve image quality, but the computational cost is too high. To improve the reconstruction details of efficient super-resolution reconstruction, we propose a Symmetric Visual Attention Network (SVAN) by applying large receptive fields. The SVAN decomposes a large kernel convolution into three different combinations of convolution operations and combines them with an attention mechanism to form a Symmetric Large Kernel Attention Block (SLKAB), which forms a symmetric attention block with a bottleneck structure by the size of the receptive field in the convolution combination to extract depth features effectively as the basic component of the SVAN. Our network gets a large receptive field while minimizing the number of parameters and improving the perceptual ability of the model. The experimental results show that the proposed SVAN can obtain high-quality super-resolution reconstruction results using only about 30% of the parameters of existing SOTA methods., Comment: 13 pages,4 figures

While monomer protein structure prediction tools boast impressive accuracy, the prediction of protein complex structures remains a daunting challenge in the field. This challenge is particularly pronounced in scenarios involving complexes with protein chains from different species, such as antigen-antibody interactions, where accuracy often falls short. Limited by the accuracy of complex prediction, tasks based on precise protein-protein interaction analysis also face obstacles. In this report, we highlight the ongoing advancements of our protein complex structure prediction model, HelixFold-Multimer, underscoring its enhanced performance. HelixFold-Multimer provides precise predictions for diverse protein complex structures, especially in therapeutic protein interactions. Notably, HelixFold-Multimer achieves remarkable success in antigen-antibody and peptide-protein structure prediction, greatly surpassing AlphaFold 3. HelixFold-Multimer is now available for public use on the PaddleHelix platform, offering both a general version and an antigen-antibody version. Researchers can conveniently access and utilize this service for their development needs.

Addressing complex meteorological processes at a fine spatial resolution requires substantial computational resources. To accelerate meteorological simulations, researchers have utilized neural networks to downscale meteorological variables from low-resolution simulations. Despite notable advancements, contemporary cutting-edge downscaling algorithms tailored to specific variables. Addressing meteorological variables in isolation overlooks their interconnectedness, leading to an incomplete understanding of atmospheric dynamics. Additionally, the laborious processes of data collection, annotation, and computational resources required for individual variable downscaling are significant hurdles. Given the limited versatility of existing models across different meteorological variables and their failure to account for inter-variable relationships, this paper proposes a unified downscaling approach leveraging meta-learning. This framework aims to facilitate the downscaling of diverse meteorological variables derived from various numerical models and spatiotemporal scales. Trained at variables consisted of temperature, wind, surface pressure and total precipitation from ERA5 and GFS, the proposed method can be extended to downscale convective precipitation, potential energy, height, humidity and ozone from CFS, S2S and CMIP6 at different spatiotemporal scales, which demonstrating its capability to capture the interconnections among diverse variables. Our approach represents the initial effort to create a generalized downscaling model. Experimental evidence demonstrates that the proposed model outperforms existing top downscaling methods in both quantitative and qualitative assessments.

PurposeChronic postsurgical pain (CPSP) is a common complication after thoracic surgery and associated with long-term adverse outcomes. This study aims to develop two prediction models for CPSP after video-assisted thoracic surgery (VATS).Methods and analysisThis single-center prospective cohort study will include a total of 500 adult patients undergoing VATS lung resection (n = 350 for development and n = 150 for external validation). Patients will be enrolled continuously at The First Affiliated Hospital of Soochow University in Suzhou, China. The cohort for external validation will be recruited in another time period. The outcome is CPSP, which is defined as pain with the numerical rating scale score of 1 or higher 3 months after VATS. Univariate and multivariable logistic regression analyses will be performed to develop two CPSP prediction models based on patients' data of postoperative day 1 and day 14, respectively. For internal validation, we will use the bootstrapping validation technique. For external validation, the discrimination capability of the models will be assessed using the area under the receiver operating characteristic curve, and the calibration will be evaluated using the calibration curve and Hosmer-Lemeshow goodness-of-fit statistic. The results will be presented in model formulas and nomograms.ConclusionBased on the development and validation of the prediction models, our results contribute to early prediction and treatment of CPSP after VATS.Trial registrationChinese Clinical Trial Register (ChiCTR2200066122).

To meet the European Union's 2050 climate neutrality target, future electricity generation is expected to largely rely on variable renewable energy (VRE). VRE supply, being dependant on weather, is susceptible to changing climate conditions. Based on spatiotemporally explicit climate data under a Paris-proof climate scenario and a comprehensive energy conversion model, this study assesses the projected changes of European VRE supply from the perspective of average production, production variability, spatiotemporal complementarity, and risk of concurrent renewable energy droughts. For the period 2045–2055, we find a minor reduction in average wind and solar production for most of Europe compared to the period 1990–2010. At the country level, the impact of climate change on average VRE production is rather limited in magnitude (within ±3% for wind and ±2% for solar). The projected mid-term changes in other aspects of VRE supply are also relatively small. This suggests climate-related impacts on European VRE supply are less of a concern if the Paris-proof emission reduction pathway is strictly followed. Based on spectral analysis, we identify strong seasonal wind-solar complementarities (with an anticorrelation between -0.6 and -0.9) at the cross-regional level. This reduces the demand for seasonal storage but requires coordinated cross-border efforts to develop a pan-European transmission infrastructure. The risk of concurrent renewable energy droughts between a country and the rest of Europe remains non-negligible, even under the copperplate assumption. Central Western European countries and Poland are most vulnerable to such risk, suggesting the need for the planning of adequate flexibility resources.

The integration of renewable energy sources into power systems is expected to increase significantly in the coming decades. This can result in critical situations related to the strong variability in space and time of weather patterns. During these critical situations the power system experiences a structural shortage of energy across multiple time steps and regions, leading to Energy Not Served (ENS) events. Our research explores the relationship between six weather regimes that describe the large scale atmospheric flow and ENS events in Europe by simulating future power systems. Our results indicate that most regions have a specific weather regime that leads to the highest number of ENS events. However, ENS events can still occur during any weather regime, but with a lower probability. In particular, our findings show that ENS events in western and central European countries often coincide with either the positive Scandinavian Blocking (SB+), characterised by cold air penetrating Europe under calm weather conditions from north-eastern regions, or North Atlantic Oscillation (NAO+) weather regime, characterised by westerly flow and cold air in the southern half of Europe. Additionally, we found that the relative impact of one of these regimes reaches a peak 10 days before ENS events in these countries. In Scandinavian and Baltic countries, on the other hand, our results indicate that the relative prevalence of the negative Atlantic Ridge (AR-) weather regime is higher during and leading up to the ENS event.

Reliable estimates and forecasts of Photovoltaic (PV) power output form a fundamental basis to support its large-scale integration. This is recognized in literature, where a growing amount of studies deal with the development of PV power estimation and forecasting models. In particular, machine learning techniques received significant attention in the past decade. Yet, the importance of predictor variables are consistently ignored in such developments and as a result those models fail to acknowledge the value of including physics-based models. In this study we quantify the value of predictor variables for PV power estimation and forecasting, assess deficiencies in estimation and forecasting models, and introduce a number of pre-processing steps to improve the overall estimation or forecasting performance. To this end, we use common physical models to create so-called expert variables and test their impact on the performance of single-point and probabilistic models. In addition, we investigate the optimal selection of predictor variables for PV power estimation and forecasting. By means of a sensitivity analysis, the paper shows how the value of expert variables is affected by the tilt angle of the PV system. To allow for a deeper insight into the importance of predictor variables, two case studies in different climate regions are considered in the numerical evaluation.

Background: There lacks a sufficient research on the tumorigenesis of clear cell renal cell carcinoma (ccRCC), causing that the prognosis of ccRCC was not effectively improved. Micall2 contributes to the malignancy of cancer. Moreover, Micall2 is considered a typical cell mobility-promoting factor. However, the relationship between Micall2 and ccRCC malignancy is unknown. Methods: In this study, we first investigated the expression patterns of Micall2 in ccRCC tissues and cell lines. Next, we explored the in vitro and in vivo roles of Micall2 in ccRCC tumorigenesis based on ccRCC cell lines with different Micall2 expression and gene manipulation assays. Results: Our study showed that ccRCC tissues and cell lines expressed higher level of Micall2 than paracancerous tissues and normal renal tubular epithelial cell, and Micall2 expression was overexpressed on cancerous tissue with significant metastasis and enlargement. Among three ccRCC cell lines, the expression of Micall2 was the highest in 786-O cells and the lowest in CAKI-1 cells. Moreover, 786-O cells showed the highest malignancy in vitro and in vivo (including proliferation, migration, invasion, reduced E-cadherin expression and tumorigenicity of nude mice in vivo), while CAKI-1 cells showed the contrary results. Furthermore, the upregulated Micall2 by Gene overexpression promoted the proliferation, migration and invasion of ccRCC cells while the downregulated Micall2 by Gene silencing showed the opposite effect. Conclusion: Micall2, as a pro-tumorigenic gene marker of ccRCC, contributes the malignancy of ccRCC.

Utilizing the spatial heterogeneity and climate periodicity of various available renewable energy sources can enhance the multienergy complementarity, which will further reduce the energy storage demand and contributes to the "virtual energy storage gain." In this paper, we propose a spatiotemporal coordination method based on spectral analysis for a wind-PV-hydropower system that targets the maximum virtual energy storage gain. The complementary effect of hydropower on wind and PV power can be seen as changes in the regulation ability resulting from the hydropower construction development as well as a decreased variance in the total system production output. This method is used to determine the optimal coordination distance of multiple energy sources that are matched over different periods representing typical climate variation. A case study in the Yalong River basin in China obtains the best range of hydropower bundling surrounding wind power and PV power stations under different hydropower construction planning scenarios, and it reveals that increased regulation capacity of hydropower will reduce the optimal coordination distance but can achieve more energy balance if both actual storage and virtual storage is accounted for. This approach can realize energy delivery with the optimal coordination distance to meet intensive and efficient development needs, which can provide guidance and support for the planning and construction of wind-PV-hydropower storage systems., QC 20230105QC 20230105

Background: There lacks a sufficient research on the tumorigenesis of clear cell renal cell carcinoma (ccRCC), causing that the prognosis of ccRCC was not effectively improved. Micall2 contributes to the malignancy of cancer. Moreover, Micall2 is considered a typical cell mobility-promoting factor. However, the relationship between Micall2 and ccRCC malignancy is unknown. Methods: In this study, we first investigated the expression patterns of Micall2 in ccRCC tissues and cell lines. Next, we explored the in vitro and in vivo roles of Micall2 in ccRCC tumorigenesis based on ccRCC cell lines with different Micall2 expression and gene manipulation assays. Results: Our study showed that ccRCC tissues and cell lines expressed higher level of Micall2 than paracancerous tissues and normal renal tubular epithelial cell, and Micall2 expression was overexpressed on cancerous tissue with significant metastasis and enlargement. Among three ccRCC cell lines, the expression of Micall2 was the highest in 786-O cells and the lowest in CAKI-1 cells. Moreover, 786-O cells showed the highest malignancy in vitro and in vivo (including proliferation, migration, invasion, reduced E-cadherin expression and tumorigenicity of nude mice in vivo), while CAKI-1 cells showed the contrary results. Furthermore, the upregulated Micall2 by Gene overexpression promoted the proliferation, migration and invasion of ccRCC cells while the downregulated Micall2 by Gene silencing showed the opposite effect. Conclusion: Micall2, as a pro-tumorigenic gene marker of ccRCC, contributes the malignancy of ccRCC., identifier:https://doi.org/10.33160/yam.2023.02.021

Depressive symptoms are common among perimenopausal women with breast cancer having modified radical mastectomy. Esketamine exerts antidepressant effects. This study aims to assess whether an intraoperative sub-anesthetic dose of esketamine prevents postoperative depressive symptoms in these patients. In this randomized, triple-blinded, placebo-controlled trial, we will enroll 130 perimenopausal women (aged 45-60 years) with breast cancer undergoing unilateral modified radical mastectomy. Patients will be randomly assigned with a 1:1 ratio to receive either esketamine (0.25 mg/kg i.v.) or normal saline after anesthesia induction and before skin incision. The primary outcome is the incidence of depressive symptoms at day 30 postoperatively, assessed using the Beck's Depression Inventory (BDI). Secondary outcomes include incidence of depressive symptoms and BDI scores at day 1, 3, and 180 postoperatively, anxiety symptoms and scores at day 1, 3, 30, and 180 postoperatively, pain intensity and quality of recovery at day 1 and 2 postoperatively, nausea and vomiting within 48 hours postoperatively, length of postoperative hospital stay, and cancer-specific outcomes. Data will be analyzed in the modified intention-to-treat population. This is the first trial to evaluate the effects of a sub-anesthetic dose of esketamine on depressive symptoms in perimenopausal women after modified radical mastectomy. The results of this study will help to improve their mental health and recovery after breast cancer surgery. Chinese Clinical Trial Registry (ChiCTR2200064348).

Light has profoundly impacted modern medicine and healthcare, with numerous luminescent agents and imaging techniques currently being used to assess health and treat diseases. As an emerging concept in luminescence, aggregation-induced emission (AIE) has shown great potential in biological applications due to its advantages in terms of brightness, biocompatibility, photostability, and positive correlation with concentration. This review provides a comprehensive summary of AIE luminogens applied in imaging of biological structure and dynamic physiological processes, disease diagnosis and treatment, and detection and monitoring of specific analytes, followed by representative works. Discussions on critical issues and perspectives on future directions are also included. This review aims to stimulate the interest of researchers from different fields, including chemistry, biology, materials science, medicine, etc., thus promoting the development of AIE in the fields of life and health.

Background: There lacks a sufficient research on the tumorigenesis of clear cell renal cell carcinoma (ccRCC), causing that the prognosis of ccRCC was not effectively improved. Micall2 contributes to the malignancy of cancer. Moreover, Micall2 is considered a typical cell mobility-promoting factor. However, the relationship between Micall2 and ccRCC malignancy is unknown. Methods: In this study, we first investigated the expression patterns of Micall2 in ccRCC tissues and cell lines. Next, we explored the in vitro and in vivo roles of Micall2 in ccRCC tumorigenesis based on ccRCC cell lines with different Micall2 expression and gene manipulation assays. Results: Our study showed that ccRCC tissues and cell lines expressed higher level of Micall2 than paracancerous tissues and normal renal tubular epithelial cell, and Micall2 expression was overexpressed on cancerous tissue with significant metastasis and enlargement. Among three ccRCC cell lines, the expression of Micall2 was the highest in 786-O cells and the lowest in CAKI-1 cells. Moreover, 786-O cells showed the highest malignancy in vitro and in vivo (including proliferation, migration, invasion, reduced E-cadherin expression and tumorigenicity of nude mice in vivo), while CAKI-1 cells showed the contrary results. Furthermore, the upregulated Micall2 by Gene overexpression promoted the proliferation, migration and invasion of ccRCC cells while the downregulated Micall2 by Gene silencing showed the opposite effect. Conclusion: Micall2, as a pro-tumorigenic gene marker of ccRCC, contributes the malignancy of ccRCC.

Black soldier fly have been shown to be one of the optimal alternatives to fishmeal, but there are few reports on the effects of zymolytic black soldier fly larvae (ZBSFL) on the growth and digestion of crustaceans. An 8-week feeding trial was conducted to evaluate the effects of different replacement levels of ZBFLS on growth performance, body composition, and digestive enzyme activity of the mud crab larvae. Four diets were formulated by replacing fishmeal with 0%, 5%, 10% and 15% ZBSFL in the basal diet. Crab larvae were randomly divided into four groups of three replicates each and fed twice daily. The results showed that the SR of crab larvae was higher than that of the no-substitution group when the substitution rate reached 5% (P < 0.05). There was no significant change in SR when the substitution rate was further increased. Weight growth rate and Specific growth rate were similar, both highest at 10% substitution ratio. The crude protein content of whole crab larvae gradually increased as the proportion of FM substituted by ZBSFL increased. The lipid content of whole crab larvae in the 5% substitution ratio group was significantly higher than that in all other groups (P < 0.05). Meanwhile. The activities of amylase, protease and lipase gradually increased. In this experiment, when the percentage of ZBSFL substitution for FM reached 10%, its growth performance was optimal, with higher SR, less negative effects and more balanced indicators in all aspects. When the substitution rate was further increased, it might increase the digestive burden of the crab and negatively affect its growth.

Non-Hermitian systems are known for their intriguing topological properties, which underpin various exotic physical phenomena. Exceptional points, in particular, play a pivotal role in fine-tuning these systems for optimal device functionality and material characteristics. These points can give rise to exceptional surfaces with embedded lower-dimensional non-isolated singularities. Here we introduce a topological classification for non-defective intersection lines of exceptional surfaces, where exceptional surfaces intersect transversally. We achieve this classification by constructing a quotient space of an order-parameter space under equivalence relations of eigenstates. We unveil that the fundamental group of these gapless structures is a non-Abelian group on three generators. This classification not only reveals a unique form of non-Hermitian gapless phases featuring a chain of non-defective intersection lines but also predicts the unexpected existence of topological edge states in one-dimensional lattice models protected by the intersection singularities. Our classification opens avenues for realizing robust topological phases. © 2023, Springer Nature Limited.

In non-Hermitian systems, the eigenvalues near exceptional points of order N (EPNs) usually exhibit E A1/N dispersion/splitting under a small perturbation A. Such high sensitivity to perturbation makes them ideal candidates for sensors. However, E - A1/m dispersions with m = 1, 2, ... , N - 1 are also possible for EPNs. Using a transposed Jordan block (TJB) matrix H0 as a representative Hamiltonian, we present general rules that provide a unified understanding of the splitting behaviors of EPNs in diverse systems. Specifically, when the k-diagonal entries (H0)i,i+k of H0 are perturbed by A, we observe a dispersion of the form E - A1/m, where m = k + 1. The phase rigidity's exponent x and discriminant number y together can serve as topological invariants to completely characterize an EP N with E - A1/m splitting, and they are proportional, i.e., x = v/N = (N - 1)/m. The results are demonstrated experimentally using electrical circuits. Next, we demonstrate the applicability of the splitting rules to general non-TJB Hamiltonians that exhibit an EPN through an example. Moreover, we observe that the splitting of EPNs can lead to the emergence of lower-order EP structures, such as EP2 ellipses in the parameter space. We find that the EP2 ellipses exhibit two types of coalescence: with and without an increase in the EP order.

This paper introduces a three-layer hierarchical model-based approach for network-wide traffic signal control, which consists of a region coordination layer, an intersection coordination layer and a local signal control layer. The hierarchical control problem is formulated by two model predictive control (MPC) schemes. The upper-level control deals with the region coordination problem, which considers the cycle length optimization for different regions based on the macroscopic fundamental diagram. Under the guidance of the upper level MPC, a distributed MPC is developed to address both the green split optimization at each local intersection and the intersection coordination. For computational efficiency, the network is decomposed into separated intersections while the Lagrangian algorithm is developed for intersection coordination. Experimental results on the Nguyen-Dupuis network prove that our proposed three-layer hierarchical control approach has great potential in reducing the computational complexity while maintaining the overall performance (such as total travel time) of the traffic network. © 2023 Hong Kong Society for Transportation Studies Limited.

Jing-Hui Hu,1,2,&ast; Hai-Jing Shi,1,2,&ast; Zhen-Yu Han,3,&ast; Hong Liu,4 Fu-Hai Ji,1,2 Ke Peng1,2 1Departments of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China; 2Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, People’s Republic of China; 3Department of Thoracic Surgery, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China; 4Department of Anesthesiology and Pain Medicine, University of California Davis Health, Sacramento, CA, USA&ast;These authors contributed equally to this workCorrespondence: Ke Peng, Department of Anesthesiology, First Affiliated Hospital of Soochow University, 188 Shizi St, Suzhou, Jiangsu, 215006, People’s Republic of China, Tel +86-159-6215-5989, Email pengke0422@163.comPurpose: Chronic postsurgical pain (CPSP) is a common complication after thoracic surgery and associated with long-term adverse outcomes. This study aims to develop two prediction models for CPSP after video-assisted thoracic surgery (VATS).Methods and Analysis: This single-center prospective cohort study will include a total of 500 adult patients undergoing VATS lung resection (n = 350 for development and n = 150 for external validation). Patients will be enrolled continuously at The First Affiliated Hospital of Soochow University in Suzhou, China. The cohort for external validation will be recruited in another time period. The outcome is CPSP, which is defined as pain with the numerical rating scale score of 1 or higher 3 months after VATS. Univariate and multivariable logistic regression analyses will be performed to develop two CPSP prediction models based on patients’ data of postoperative day 1 and day 14, respectively. For internal validation, we will use the bootstrapping validation technique. For external validation, the discrimination capability of the models will be assessed using the area under the receiver operating ch

Chiral zeroth Landau levels are topologically protected bulk states. In particle physics and condensed matter physics, the chiral zeroth Landau level plays a significant role in breaking chiral symmetry and gives rise to the chiral anomaly. Previous experimental works on such chiral Landau levels are mainly based on three-dimensional Weyl degeneracies coupled with axial magnetic fields. Their realizations using two-dimensional Dirac point systems, being more promising for future applications, were never experimentally realized before. Here we propose an experimental scheme for realizing chiral Landau levels in a two-dimensional photonic system. By introducing an inhomogeneous effective mass through breaking local parity-inversion symmetries, a synthetic in-plane magnetic field is generated and coupled with the Dirac quasi-particles. Consequently, the zeroth-order chiral Landau levels can be induced, and the one-way propagation characteristics are experimentally observed. In addition, the robust transport of the chiral zeroth mode against defects in the system is also experimentally tested. Our system provides a new pathway for the realization of chiral Landau levels in two-dimensional Dirac cone systems, and may potentially be applied in device designs utilizing the chiral response and transport robustness. © 2023, The Author(s).

Lin-Lin Liu,1,2,&ast; Jing-Hui Hu,1,2,&ast; Jing-Jing Pan,1,3,&ast; Hong Liu,4 Fu-Hai Ji,1,2 Ke Peng1,2 1Departments of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China; 2Institute of Anesthesiology, Soochow University, Suzhou, Jiangsu, People’s Republic of China; 3Department of Anesthesiology, Guanyun People’s Hospital, Lianyungang, Jiangsu, People’s Republic of China; 4Department of Anesthesiology and Pain Medicine, University of California Davis Health, Sacramento, CA, USA&ast;These authors contributed equally to this workCorrespondence: Ke Peng, Department of Anesthesiology, First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou, Jiangsu, 215006, People’s Republic of China, Tel +86-159-6215-5989, Email pengke0422@163.comBackground: Depressive symptoms are common among perimenopausal women with breast cancer having modified radical mastectomy. Esketamine exerts antidepressant effects. This study aims to assess whether an intraoperative sub-anesthetic dose of esketamine prevents postoperative depressive symptoms in these patients.Methods: In this randomized, triple-blinded, placebo-controlled trial, we will enroll 130 perimenopausal women (aged 45− 60 years) with breast cancer undergoing unilateral modified radical mastectomy. Patients will be randomly assigned with a 1:1 ratio to receive either esketamine (0.25 mg/kg i.v.) or normal saline after anesthesia induction and before skin incision. The primary outcome is the incidence of depressive symptoms at day 30 postoperatively, assessed using the Beck’s Depression Inventory (BDI). Secondary outcomes include incidence of depressive symptoms and BDI scores at day 1, 3, and 180 postoperatively, anxiety symptoms and scores at day 1, 3, 30, and 180 postoperatively, pain intensity and quality of recovery at day 1 and 2 postoperatively, nausea and vomiting within 48 hours postoperatively, length of postoperative h

Yin-Ting Yang,1,2,&ast; Xi-Ya Jiang,1,2,&ast; Hong-Liang Xu,3,&ast; Guo Chen,1,2,&ast; Sen-Lin Wang,4 He-Ping Zhang,3 Lin Hong,1,2 Qin-Qin Jin,1,2 Hui Yao,1,2 Wei-Yu Zhang,1,2 Yu-Ting Zhu,1,2 Jie Mei,1,2 Lu Tian,1,2 Jie Ying,1,2 Jing-Jing Hu,5 Shu-Guang Zhou1,2 1Department of Gynecology and Obstetrics, Maternity and Child Healthcare Hospital Affiliated to Anhui Medical University, Anhui Province Maternity and Child Healthcare Hospital, Hefei, Anhui, 230001, People’s Republic of China; 2Department of Gynecology and Obstetrics, The Fifth Clinical College of Anhui Medical University, Hefei, Anhui, 230032, People’s Republic of China; 3Department of Pathology, Anhui Province Maternity and Child Healthcare Hospital, Hefei, Anhui, 230001, People’s Republic of China; 4Department of Medical Genetics Center, Anhui Province Maternity and Child Healthcare Hospital, Hefei, Anhui, 230001, People’s Republic of China; 5Department of Gynecology and Obstetrics, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230022, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Shu-Guang Zhou, Department of Gynecology, Anhui Medical University Affiliated Maternity and Child Healthcare Hospital, The Fifth Clinical College of Anhui Medical University, Anhui Province Maternity and Child Healthcare Hospital, No. 15, Yi-Min Street, Lu-Yang District, Hefei, Anhui, 230001, People’s Republic of China, Email zhoushuguang@ahmu.edu.cn Jing-Jing Hu, Department of Gynecology and Obstetrics, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230022, People’s Republic of China, Email janejj55@126.comBackground: Endometriosis, a common gynecological condition, can cause symptoms such as dysmenorrhea, infertility, and abnormal bleeding, which can negatively affect a woman’s quality of life. In the current study, the pathophysiological mechanisms of endometriosis are unknown, but this study

The increasing importance of renewable energy supply in the transition to carbon neutral energy systems highlights the need of an adequate understanding of the associated risks to energy supply. Variable renewable energy sources are inherently dependent on meteorological conditions, and as such could provide insufficient energy for certain periods of time. In this study, a characterization is performed on such energy drought events in the Netherlands. ERA-5 reanalysis and MERRA-2 climate data is used to model the performance of solar PV and wind power generation. Statistical approaches are used to investigate these events on provincial, sub-national and national scale. It has been found that the province least susceptible to energy drought occurrence is Zeeland, whereas Gelderland, Limburg, Noord-Brabant, Overijssel and Utrecht are most likely to experience energy droughts. Additionally, copula theory is used to investigate probabilities of the co-occurrence of energy drought events between regions and technologies. When provinces are aggregated to sub-national regions, this research shows that constructed copula models capture tail events in the distribution of power generation in the form of energy droughts well. A co-occurrence between 9.61% and 14.9% has been found for ED events between the two defined sub-national regions, with return periods between 67.7 and 117 days. Finally, extreme value theory is applied to investigate extremely long-lasting energy droughts. Using the Peak-OverThreshold method, a VaR-95% value between 27.2 and 46 hours and a CVaR-95% value between 41.6 and 58.1 hours has been found for extreme energy drought duration in the Netherlands. Furthermore, 50- year and 100-year ED durations are found to be between 84 and 99 hours and 91 and 107 hours, respectively. The results of this study can be incorporated in the planning of future renewable energy installations, and for grid operators to determine and manage the risk of black outs more accur

Dandan Wu,1,&ast; Junxiao Cong,1,&ast; Jiali Wei,1 Jing Hu,1 Wenhao Sun,1 Wei Ran,2 Chenghui Liao,1 Housheng Zheng,1 Liang Ye1 1Department of Immunology, International Cancer Center, Shenzhen University Health Science Center, Shenzhen, People’s Republic of China; 2State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Liang Ye, Department of Immunology, International Cancer Center, Shenzhen University Health Science Center, Shenzhen, 518055, People’s Republic of China, Tel +86-0755-26631420, Fax +86-0755-86671943, Email liangyeszu@163.comBackground: The emergence of the coronavirus disease 2019 (COVID-19) pandemic and the new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants of concern (VOCs) requires the continuous development of safe, effective, and affordable prevention and therapeutics. Nanobodies have demonstrated antiviral activity against a variety of viruses, providing a new candidate for the prevention and treatment of SARS-CoV-2 and its variants.Methods: SARS-CoV-2 glycoprotein spike 1 subunit (S1) was selected as the target antigen for nanobody screening of a naïve phage display library. We obtained a nanobody, named Nb-H6, and then determined its affinity, inhibition, and stability by ELISA, Competitive ELISA, and Biolayer Interferometry (BLI). Infection assays of authentic and pseudotyped SARS-CoV-2 were performed to evaluate the neutralization of Nb-H6. The structure and mechanism of action were investigated by AlphaFold, docking, and residue mutation assays.Results: We isolated and characterized a nanobody, Nb-H6, which exhibits a broad affinity for S1 and the receptor binding domain (RBD) of SARS-CoV-2, or Alpha (B.1.1.7), Delta

Fengli Huang,1,&ast; Qun Zhang,2,&ast; Jie Xiao,3,&ast; Xin Zhang,1 Xingzhi Han,3 Xiao Shi,3 Jing Hu,2 Li Li,2 Xiaoping Qian1,2 1Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China; 2Department of Oncology, Nanjing Drum Tower Hospital, Nanjing, People’s Republic of China; 3Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiaoping Qian, Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, People’s Republic of China, Tel +86-13951743162, Fax +86-25-68182342, Email xiaopingqian@nju.edu.cnPurpose: Recent studies have shown that traditional Chinese medicine (TCM), such as gambogic acid (GA), is involved in the regulation of tumor immune microenvironment and can be combined with other anti-tumor treatment strategies. Here, we used GA as an adjuvant to construct a nano-vaccine to improve the anti-tumor immune response of colorectal cancer (CRC).Materials and Methods: We used a previously reported two-step emulsification method to obtain poly (lactic-co-glycolic acid) /GA nanoparticles (PLGA/GA NPs), and then CT26 colon cancer cell membrane (CCM) was used to obtain CCM-PLGA/GA NPs. This novel nano-vaccine, CCM-PLGA/GA NPs, was co-synthesized with GA as an adjuvant and neoantigen provided by CT26 CCM. We further confirmed the stability, tumor targeting, and cytotoxicity of CCM-PLGA/GA NPs. The regulatory effect on the tumor immune microenvironment, the anti-tumor efficacy, and the combined anti-tumor efficacy with anti-PD-1 monoclonal Antibodies (mAbs) of this novel nano-vaccine was also detected in vivo.Results: We successfully constructed the CCM-PLGA/GA NPs. In vitro and in vivo tests sho

Exceptional points are a unique feature of non-Hermitian systems at which the eigenvalues and corresponding eigenstates of a Hamiltonian coalesce. Many intriguing physical phenomena arise from the topology of exceptional points, such as bulk Fermi arcs and the braiding of eigenvalues. Here we report that a structurally richer degeneracy morphology, known as the swallowtail catastrophe in singularity theory, can naturally exist in non-Hermitian systems with both parity–time and pseudo-Hermitian symmetries. For the swallowtail, three different types of singularity exist at the same time and interact with each other—an isolated nodal line, a pair of exceptional lines of order three and a non-defective intersection line. Although these singularities seem independent, they are stably connected at a single point—the vertex of the swallowtail—through which transitions can occur. We implement such a system in a non-reciprocal circuit and experimentally observe the degeneracy features of the swallowtail. Based on the frame rotation and deformation of eigenstates, we further demonstrate that the various transitions are topologically protected.

Thermal issue is a major concern in 3D integrated circuit (IC) design. Thermal optimization of 3D IC often requires massive expensive PDE simulations. Neural network-based thermal prediction models can perform real-time prediction for many unseen new designs. However, existing works either solve 2D temperature fields only or do not generalize well to new designs with unseen design configurations (e.g., heat sources and boundary conditions). In this paper, for the first time, we propose DeepOHeat, a physics-aware operator learning framework to predict the temperature field of a family of heat equations with multiple parametric or non-parametric design configurations. This framework learns a functional map from the function space of multiple key PDE configurations (e.g., boundary conditions, power maps, heat transfer coefficients) to the function space of the corresponding solution (i.e., temperature fields), enabling fast thermal analysis and optimization by changing key design configurations (rather than just some parameters). We test DeepOHeat on some industrial design cases and compare it against Celsius 3D from Cadence Design Systems. Our results show that, for the unseen testing cases, a well-trained DeepOHeat can produce accurate results with $1000\times$ to $300000\times$ speedup., Comment: Camera-ready for ACM/IEEE Design Automation Conference (DAC) 2023

Reliable estimates and forecasts of Photovoltaic (PV) power output form a fundamental basis to support its large-scale integration. This is recognized in literature, where a growing amount of studies deal with the development of PV power estimation and forecasting models. In particular, machine learning techniques received significant attention in the past decade. Yet, the importance of predictor variables are consistently ignored in such developments and as a result those models fail to acknowledge the value of including physics-based models. In this study we quantify the value of predictor variables for PV power estimation and forecasting, assess deficiencies in estimation and forecasting models, and introduce a number of pre-processing steps to improve the overall estimation or forecasting performance. To this end, we use common physical models to create so-called expert variables and test their impact on the performance of single-point and probabilistic models. In addition, we investigate the optimal selection of predictor variables for PV power estimation and forecasting. By means of a sensitivity analysis, the paper shows how the value of expert variables is affected by the tilt angle of the PV system. To allow for a deeper insight into the importance of predictor variables, two case studies in different climate regions are considered in the numerical evaluation.

© 2020 Acta Materialia Inc. Radiation damage of structural materials leads to mechanical property degradation, eventually inducing failure. Secondary-phase dispersoids or other defect sinks are often added to materials to boost their radiation resistance. We demonstrate that a metal composite made by adding 1D carbon nanotubes (CNTs) to aluminum (Al) exhibits superior radiation resistance. In situ ion irradiation with transmission electron microscopy (TEM) and atomistic simulations together reveal the mechanisms of rapid defect migration to CNTs, facilitating defect recombination and enhancing radiation tolerance. The origin of this effect is an evolving stress gradient in the Al matrix resulting from CNT transformation under irradiation, and the stability of resulting carbides. Extreme value statistics of large defect behavior in our simulations highlight the role of CNTs in reducing accumulated damage. This approach to controlling defect migration represents a promising opportunity to enhance the radiation resistance of nuclear materials without detrimental effects.

In the field of clinical medicine, computed tomography (CT) is an effective medical imaging modality for the diagnosis of various pathologies. Compared with X-ray images, CT images can provide more information, including multi-planar slices and three-dimensional structures for clinical diagnosis. However, CT imaging requires patients to be exposed to large doses of ionizing radiation for a long time, which may cause irreversible physical harm. In this paper, we propose an Uncertainty-aware MedNeRF (UMedNeRF) network based on generated radiation fields. The network can learn a continuous representation of CT projections from 2D X-ray images by obtaining the internal structure and depth information and using adaptive loss weights to ensure the quality of the generated images. Our model is trained on publicly available knee and chest datasets, and we show the results of CT projection rendering with a single X-ray and compare our method with other methods based on generated radiation fields.

Controlling the degree of stylization in the Neural Style Transfer (NST) is a little tricky since it usually needs hand-engineering on hyper-parameters. In this paper, we propose the first deep Reinforcement Learning (RL) based architecture that splits one-step style transfer into a step-wise process for the NST task. Our RL-based method tends to preserve more details and structures of the content image in early steps, and synthesize more style patterns in later steps. It is a user-easily-controlled style-transfer method. Additionally, as our RL-based model performs the stylization progressively, it is lightweight and has lower computational complexity than existing one-step Deep Learning (DL) based models. Experimental results demonstrate the effectiveness and robustness of our method., Comment: Accepted by IJCAI 2023. The contributions of Chengming Feng and Jing Hu to this paper were equal. arXiv admin note: text overlap with arXiv:2309.13672

Most existing Image-to-Image Translation (I2IT) methods generate images in a single run of a deep learning (DL) model. However, designing such a single-step model is always challenging, requiring a huge number of parameters and easily falling into bad global minimums and overfitting. In this work, we reformulate I2IT as a step-wise decision-making problem via deep reinforcement learning (DRL) and propose a novel framework that performs RL-based I2IT (RL-I2IT). The key feature in the RL-I2IT framework is to decompose a monolithic learning process into small steps with a lightweight model to progressively transform a source image successively to a target image. Considering that it is challenging to handle high dimensional continuous state and action spaces in the conventional RL framework, we introduce meta policy with a new concept Plan to the standard Actor-Critic model, which is of a lower dimension than the original image and can facilitate the actor to generate a tractable high dimensional action. In the RL-I2IT framework, we also employ a task-specific auxiliary learning strategy to stabilize the training process and improve the performance of the corresponding task. Experiments on several I2IT tasks demonstrate the effectiveness and robustness of the proposed method when facing high-dimensional continuous action space problems. Our implementation of the RL-I2IT framework is available at https://github.com/Algolzw/SPAC-Deformable-Registration.

Search query classification, as an effective way to understand user intents, is of great importance in real-world online ads systems. To ensure a lower latency, a shallow model (e.g. FastText) is widely used for efficient online inference. However, the representation ability of the FastText model is insufficient, resulting in poor classification performance, especially on some low-frequency queries and tailed categories. Using a deeper and more complex model (e.g. BERT) is an effective solution, but it will cause a higher online inference latency and more expensive computing costs. Thus, how to juggle both inference efficiency and classification performance is obviously of great practical importance. To overcome this challenge, in this paper, we propose knowledge condensation (KC), a simple yet effective knowledge distillation framework to boost the classification performance of the online FastText model under strict low latency constraints. Specifically, we propose to train an offline BERT model to retrieve more potentially relevant data. Benefiting from its powerful semantic representation, more relevant labels not exposed in the historical data will be added into the training set for better FastText model training. Moreover, a novel distribution-diverse multi-expert learning strategy is proposed to further improve the mining ability of relevant data. By training multiple BERT models from different data distributions, it can respectively perform better at high, middle, and low-frequency search queries. The model ensemble from multi-distribution makes its retrieval ability more powerful. We have deployed two versions of this framework in JD search, and both offline experiments and online A/B testing from multiple datasets have validated the effectiveness of the proposed approach.