Fumarate hydratase-deficient renal cell carcinoma (FH-deficient RCC) is a rare but lethal subtype of RCC. Little is known about the genomic profile of FH-deficient RCC, and the therapeutic options ...for advanced disease are limited. To this end, we performed a comprehensive genomics study to characterize the genomic and epigenomic features of FH-deficient RCC.
Integrated genomic, epigenomic, and molecular analyses were performed on 25 untreated primary FH-deficient RCCs. Complete clinicopathologic and follow-up data of these patients were recorded.
We identified that FH-deficient RCC manifested low somatic mutation burden (median 0.58 mutations per megabase), but with frequent somatic copy-number alterations. The majority of FH-deficient RCCs were characterized by a CpG sites island methylator phenotype, displaying concerted hypermethylation at numerous CpG sites in genes of transcription factors, tumor suppressors, and tumor hallmark pathways. However, a few cases (20%) with low metastatic potential showed relatively low DNA methylation levels, indicating the heterogeneity of methylation pattern in FH-deficient RCC. Moreover, FH-deficient RCC is potentially highly immunogenic, characterized by increased tumor T-cell infiltration but high expression of immune checkpoint molecules in tumors. Clinical data further demonstrated that patients receiving immune checkpoint blockade-based treatment achieved improved progression-free survival over those treated with antiangiogenic monotherapy (median, 13.3 vs. 5.1 months;
= 0.03).
These results reveal the genomic features and provide new insight into potential therapeutic strategies for FH-deficient RCC.
Global horizontal irradiance (GHI) plays a vital role in estimating solar energy resources, which are used to generate sustainable green energy. In order to estimate GHI with high spatial resolution, ...a quantitative irradiance estimation network, named QIENet, is proposed. Specifically, the temporal and spatial characteristics of remote sensing data of the satellite Himawari-8 are extracted and fused by recurrent neural network (RNN) and convolution operation, respectively. Not only remote sensing data, but also GHI-related time information (hour, day, and month) and geographical information (altitude, longitude, and latitude), are used as the inputs of QIENet. The satellite spectral channels B07 and B11–B15 and time are recommended as model inputs for QIENet according to the spatial distributions of annual solar energy. Meanwhile, QIENet is able to capture the impact of various clouds on hourly GHI estimates. More importantly, QIENet does not overestimate ground observations and can also reduce RMSE by 27.51%/18.00%, increase R2 by 20.17%/9.42%, and increase r by 8.69%/3.54% compared with ERA5/NSRDB. Furthermore, QIENet is capable of providing a high-fidelity hourly GHI database with spatial resolution 0.02°×0.02° (approximately 2km×2km) for many applied energy fields.
•A quantitative irradiance estimation network, named QIENet, using recurrent neural network is proposed.•The influence of the satellite Himawari-8 spectral channels, time, and geographical information on QIENet is investigated.•QIENet is capable of providing a high-fidelity hourly GHI database with spatial resolution 0.02°×0.02°.
Celiac disease exhibits a higher prevalence among patients with coronavirus disease 2019. However, the potential influence of COVID-19 on celiac disease remains uncertain. Considering the significant ...association between gut microbiota alterations, COVID-19 and celiac disease, the two-step Mendelian randomization method was employed to investigate the genetic causality between COVID-19 and celiac disease, with gut microbiota as the potential mediators. We employed the genome-wide association study to select genetic instrumental variables associated with the exposure. Subsequently, these variables were utilized to evaluate the impact of COVID-19 on the risk of celiac disease and its potential influence on gut microbiota. Employing a two-step Mendelian randomization approach enabled the examination of potential causal relationships, encompassing: 1) the effects of COVID-19 infection, hospitalized COVID-19 and critical COVID-19 on the risk of celiac disease; 2) the influence of gut microbiota on celiac disease; and 3) the mediating impact of the gut microbiota between COVID-19 and the risk of celiac disease. Our findings revealed a significant association between critical COVID-19 and an elevated risk of celiac disease (inverse variance weighted IVW: P = 0.035). Furthermore, we observed an inverse correlation between critical COVID-19 and the abundance of Victivallaceae (IVW: P = 0.045). Notably, an increased Victivallaceae abundance exhibits a protective effect against the risk of celiac disease (IVW: P = 0.016). In conclusion, our analysis provides genetic evidence supporting the causal connection between critical COVID-19 and lower Victivallaceae abundance, thereby increasing the risk of celiac disease.
We investigate how the optical gain or loss (characterized by isotropic complex refractive indexes) influence the ideal Kerker scattering of exactly zero backward scattering. It was previously shown ...that, for non-magnetic homogeneous spheres with incident plane waves, either gain or loss prohibit ideal Kerker scattering, provided that only electric and magnetic multipoles of a specific order are present and contributions from other multipoles can all be made precisely zero. Here we reveal that, when two multipoles of a fixed order are perfectly matched in terms of both phase and magnitude, multipoles of at least the next two orders cannot possibly be tuned to be all precisely zero or even perfectly matched, and consequently cannot directly produce ideal Kerker scattering. Moreover, we further demonstrate that, when multipoles of different orders are simultaneously taken into consideration, loss or gain can serve as helpful rather than harmful contributing factors, for the elimination of backward scattering.
Polycystic ovarian syndrome (PCOS) is a common reproductive disorder that affects a considerable number of women worldwide. It is accompanied by irregular menstruation, hyperandrogenism, metabolic ...abnormalities, reproductive disorders and other clinical symptoms, which seriously endangers women's physical and mental health. The etiology and pathogenesis of PCOS are not completely clear, but it is hypothesized that immune system may play a key role in it. However, previous studies investigating the connection between immune cells and PCOS have produced conflicting results.
Mendelian randomization (MR) is a powerful study design that uses genetic variants as instrumental variables to enable examination of the causal effect of an exposure on an outcome in observational data. In this study, we utilized a comprehensive two-sample MR analysis to examine the causal link between 731 immune cells and PCOS. We employed complementary MR methods, such as the inverse-variance weighted (IVW) method, and conducted sensitivity analyses to evaluate the reliability of the outcomes.
Four immunophenotypes were identified to be significantly associated with PCOS risk: Memory B cell AC (IVW: OR 95%: 1.1231.040 to 1.213,
= 0.003), CD39+ CD4+ %CD4+ (IVW: OR 95%: 0.8690.784 to 0.963,
= 0.008), CD20 on CD20- CD38-(IVW: OR 95%:1.2971.088 to 1.546,
= 0.004), and HLA DR on CD14- CD16+ monocyte (IVW: OR 95%:1.2251.074 to 1.397,
= 0.003). The results of the sensitivity analyses were consistent with the main findings.
Our MR analysis provides strong evidence supporting a causal association between immune cells and the susceptibility of PCOS. This discovery can assist in clinical decision-making regarding disease prognosis and treatment options, and also provides a new direction for drug development.
•Constructed a novel knowledge and data dual-driven approach (Adaptive-TgDLF) that makes full use of human knowledge and advanced deep learning techniques.•Employed adaptive learning to utilize load ...data at various locations and times, which improves the generalization ability of model.•Proposed a method to mine interpretability of the deep-learning model for load forecasting via attention matrix.•The proposed model is stronger (being 16% more accurate), more robust (the performance of the proposed model with 50% weather noise is the same as that of the previous efficient model without weather noise), easier to train (saving more than half of the training time), and requires less data.
Electrical energy is essential in today's society. Accurate electrical load forecasting is beneficial for better scheduling of electricity generation and saving electrical energy. In this paper, we propose an adaptive deep-learning load forecasting framework by integrating Transformer and domain knowledge (Adaptive-TgDLF). Adaptive-TgDLF introduces the deep-learning model Transformer and adaptive learning methods (including transfer learning for different locations and online learning for different time periods), which captures the long-term dependency of the load series, and is more appropriate for realistic scenarios with scarce samples and variable data distributions. Under the theory-guided framework, the electrical load is divided into dimensionless trends and local fluctuations. The dimensionless trends are considered as the inherent pattern of the load, and the local fluctuations are considered to be determined by the external driving forces. Adaptive learning can cope with the change of load in location and time, and can make full use of load data at different locations and times to train a more efficient model. Cross-validation experiments on different districts show that Adaptive-TgDLF is approximately 16% more accurate than the previous TgDLF model and saves more than half of the training time. Adaptive-TgDLF with 50% weather noise has the same accuracy as the previous TgDLF model without noise, which proves its robustness. We also preliminarily mine the interpretability of Transformer in Adaptive-TgDLF, which may provide future potential for better theory guidance. Furthermore, experiments demonstrate that transfer learning can accelerate convergence of the model in half the number of training epochs and achieve better performance, and online learning enables the model to achieve better results on the changing load.
Abstract
TFE3
-translocation renal cell carcinoma (
TFE3
-tRCC) is a rare and heterogeneous subtype of kidney cancer with no standard treatment for advanced disease. We describe comprehensive ...molecular characteristics of 63 untreated primary
TFE3
-tRCCs based on whole-exome and RNA sequencing.
TFE3
-tRCC is highly heterogeneous, both clinicopathologically and genotypically.
ASPSCR1-TFE3
fusion and several somatic copy number alterations, including the loss of 22q, are associated with aggressive features and poor outcomes. Apart from tumors with
MED15-TFE3
fusion, most
TFE3
-tRCCs exhibit low PD-L1 expression and low T-cell infiltration. Unsupervised transcriptomic analysis reveals five molecular clusters with distinct angiogenesis, stroma, proliferation and KRAS down signatures, which show association with fusion patterns and prognosis. In line with the aggressive nature, the high angiogenesis/stroma/proliferation cluster exclusively consists of tumors with
ASPSCR1-TFE3
fusion. Here, we describe the genomic and transcriptomic features of
TFE3
-tRCC and provide insights into precision medicine for this disease.
Geomechanical logs are of ultimate importance for subsurface description and evaluation, as well as for the exploration of underground resources, such as oil and gas, groundwater, minerals, and ...geothermal energy. Together with geological and hydrological properties, low-cost and high-accuracy models can be generated based on geomechanical parameters. However, it is challenging to directly measure geomechanical parameters, and they are usually estimated based on other measured quantities. For example, geomechanical logs may be obtained with certain empirical models from sonic logs together with prior information such as rock types, which are not readily available. Finding a way to directly estimate geomechanical logs based on easily available conventional well logs can result in significant cost savings and increased efficiency. In this article, we showed that deep learning via the long short-term memory network (LSTM) is effective in constructing an end-to-end model that takes the spatial dependence in well logs into consideration. We further proposed a physics-constrained LSTM, in which the physical mechanism behind the geomechanical parameters is utilized as a priori information. This state-of-the-art model is capable to directly estimate geomechanical logs based on easily available data, and it achieves higher prediction accuracy since the domain knowledge of the problem is considered.
The working mechanisms of complex natural systems tend to abide by concise partial differential equations (PDEs). Methods that directly mine equations from data are called PDE discovery, which ...reveals consistent physical laws and facilitates our interactions with the natural world. In this paper, an enhanced deep reinforcement-learning framework is proposed to uncover symbolically concise open-form PDEs with little prior knowledge. Particularly, based on a symbol library of basic operators and operands, a PDE can be represented by a tree structure. A structure-aware recurrent neural network agent is designed to capture structured information, and is seamlessly combined with the sparse regression method to generate open-form PDE expressions. All of the generated PDEs are evaluated by a meticulously designed reward function by balancing fitness to data and parsimony, and updated by the model-based reinforcement learning. Customized constraints and regulations are formulated to guarantee the rationality of PDEs in terms of physics and mathematics. Numerical experiments demonstrate that our framework is capable of mining open-form governing equations of several dynamic systems, even with compound equation terms, fractional structure, and high-order derivatives. This method is also applied to a real-world problem of the oceanographic system and demonstrates great potential for knowledge discovery in more complicated circumstances with exceptional efficiency and scalability.
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Identifying essential proteins from protein-protein interaction networks is important for studies on biological evolution and new drug’s development. Most of the presented criteria for prioritizing ...essential proteins only focus on a certain attribute of the proteins in the network, which suffer from information loss. In order to overcome this problem, a relatively comprehensive and effective novel method for essential proteins identification based on improved multicriteria decision making (MCDM), called essential proteins identification-technique for order preference by similarity to ideal solution (EPI-TOPSIS), is proposed. First, considering different attributes of proteins, we propose three methods from different aspects to evaluate the significance of the proteins: gene-degree centrality (GDC) for gene expression sequence; subcellular-neighbor-degree centrality (SNDC) and subcellular-in-degree centrality (SIDC) for subcellular location information and protein complexes. Then, betweenness centrality (BC) and these three methods are considered together as the multiple criteria of the decision-making model. Analytic hierarchy process is used to evaluate the weights of each criterion, and the essential proteins are prioritized by an ideal solution of MCDM, i.e., TOPSIS. Experiments are conducted on YDIP, YMIPS, Krogan and BioGRID networks. The results indicate that EPI-TOPSIS outperforms several state-of-the-art approaches for identifying the essential proteins through the performance measures.
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