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Abstract The doubly-fed induction generator (DFIG) with virtual inertia control and reactive damping control gives a renewable energy generation system inertia and damping characteristics similar to those of a thermal power plant, and the parameters of the control strategy have a direct impact on the small-signal stability of the system. This paper firstly introduces the operating characteristics and control strategies of DFIG-based damping control and virtual inertia control, establishes a small-signal model of the control-based DFIG integrated interconnected system, and investigates the effects of virtual inertia and reactive damping values on the small-signal stability of the system; then, the maximum damping ratio of the interval oscillation mode in small disturbance analysis is taken as the optimization objective, and the control parameters are the optimization variables. An optimization method of inertia and damping parameters is established for improving the small disturbance stability of the system. The results show that the optimization procedure could improve the damping ratio of the interval oscillation mode while ensuring the system frequency. The effects of virtual inertia and reactive damping values on the small signal stability of the system are investigated, and an optimal allocation model and method for virtual inertia used to improve the small disturbance stability of the system is proposed.

The aim of this study is to determine the levels of 40K radionuclides in bananas procured from different regions, calculate the annual effective dose, analyze the carcinogenic risk, and establish baseline data. Nine kinds of bananas from 6 regions in China and three kinds of bananas from 2 regions abroad were collected. After peeling, slicing, drying, crushing, sieving, and sample loading, the samples were measured and analyzed using a γ-ray spectrometer. 40K was detected in all bananas, but the concentrations of the other radionuclides were lower than the detection limit. The radioactivity concentration of 40K in Philippine imperial banana was the highest and that in Xigong banana from Guangxi was the lowest. The average 40K radioactivity of imported bananas was (472.1±59.5) Bq/kg, while that of domestic bananas was (361.9±80.7) Bq/kg. There was no significant difference between the two groups (p

Cholangiocarcinoma is a malignant tumor originating from the bile duct epithelium, with the increasing incidence year by year. Its early symptoms are atypical and the diagnosis rate is low. Most of the patients are already in advanced stage when they are diagnosed, losing the best surgery period. Currently, the conservative treatments for unresectable extrahepatic cholangiocarcinoma include stent placement, radiofrequency ablation, radiotherapy and chemotherapy, targeted and immunotherapy and other systemic treatments. But due to the high malignancy of biliary tract tumors, the tendency to develop drug resistance and the limited population benefited from the emerging treatment modalities, we urgently need to explore new treatment strategies to break this bottleneck. As a new treatment for cholangiocarcinoma, photodynamic therapy has attracted much attention for its clinical application and therapeutic effects. In this paper, we summarize the principles of photodynamic therapy, the combination of photodynamic and other therapeutic modalities, especially the combination of photodynamic with emerging immune and targeted therapies, and describe the current hotspot directions of photodynamic therapy research at home and abroad to provide reference for clinical treatment and research.

Twist phase is a nontrivial second-order phase that only exists in a partially coherent beam. Such twist phase endows the partially coherent beam with orbital angular momentum (OAM) and has unique applications such as in super-resolution imaging. However, the manipulation and the detection of the twist phase are still far from easy tasks in experiment. In this work, we present a flexible approach to generate a famous class of twisted Gaussian Schell-model (TGSM) beam with controllable twist phase by the superposition of the complex field realizations using a single phase-only spatial light modulator. The precise control of the amplitude and phase of the field realizations allows one to manipulate the strength of the twist phase easily. In addition, we show that the twist factor, a key factor that determines the strength of twist phase and the amount of OAM, can be measured by extracting the real part of the complex degree of coherence of the TGSM beam. The experiment is carried out with the help of the generalized Hanbury Brown and Twiss experiment as the generated TGSM beam obeys Gaussian statistics. The flexible control and detection of the twist phase are expected to find applications in coherence and OAM-based ghost imaging.

Recent advancements in spatial transcriptomics (ST) technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues. Despite these capabilities of the ST data, accurately dissecting spatiotemporal structures (e.g., spatial domains, temporal trajectories, and functional interactions) remains challenging. Here, we introduce a computational framework, PearlST (partial differential equation [PDE]-enhanced adversarial graph autoencoder of ST), for accurate inference of spatiotemporal structures from the ST data using PDE-enhanced adversarial graph autoencoder. PearlST employs contrastive learning to extract histological image features, integrates a PDE-based diffusion model to enhance characterization of spatial features at domain boundaries, and learns the latent low-dimensional embeddings via Wasserstein adversarial regularized graph autoencoders. Comparative analyses across multiple ST datasets with varying resolutions demonstrate that PearlST outperforms existing methods in spatial clustering, trajectory inference, and pseudotime analysis. Furthermore, PearlST elucidates functional regulations of the latent features by linking intercellular ligand-receptor interactions to most contributing genes of the low-dimensional embeddings, as illustrated in a human breast cancer dataset. Overall, PearlST proves to be a powerful tool for extracting interpretable latent features and dissecting intricate spatiotemporal structures in ST data across various biological contexts.

Abstract Background Cell functions depend on molecules organized in the cellular society. Two basic components are mRNA molecules and proteins. The interactions within and between those two components are crucial for carrying out sophisticated cell functions. The interplay can be analyzed by comparing expression levels of mRNA and proteins. This is critical for understanding the molecular interactions, (post-) transcriptional regulations and conservation of co-expression between mRNAs and proteins. By using high-throughput transcriptome and proteome data, this study aims to systematically investigate the general picture of such expression correlations. We analyze four groups of correlations: (i) transcript levels of different genes, (ii) protein levels of different genes, (iii) mRNA levels with protein levels of different genes and (iv) mRNA levels with protein levels of same genes. This helps to obtain global insights into the stability and variability of co-expression and correlation of mRNA and protein levels. Results Analysis of the simultaneous co-expression of mRNAs and proteins yields mainly weak correlations. Therefore we introduce the concept of time-delayed co-expression patterns. Based on a time-course dataset, we obtain a high fraction of time-delayed correlations. In group (i), 67% of different transcripts are significantly correlated. At the protein level (ii), 68% of different proteins are significantly correlated. Comparison of the different molecular levels results in a 74% fraction of correlated transcript and protein levels of different genes (iii) and 56% for the same genes (iv). Furthermore, a higher fraction of protein levels (simultaneously 20% and short time-delayed 29%) is correlated than at the transcript level (10% and 18% respectively). Analysis of the dynamics of the correlation shows that correlation at the transcript level is largely passed to the protein level. In contrast, specific co-expression patterns are changed in multiple ways. Conclusions Our analysis reveals that the regulation of transcription and translation contains a time-delayed component. The correlation at the protein level is more synchronous or delayed by shorter time than those at the transcript level. This supports the hypothesis that a higher degree of direct physical interactions require a higher synchronicity between the interacting partners. The conservation of correlation between the transcript level (i) and the protein level (ii) sheds light on the processes underlying transcription, translation and regulation. A future investigation of the conditions of conservation will give comprehensive insights in the complexity of the regulatory mechanisms.

Abstract Background Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level. Results Inspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s) for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles. Conclusion This modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge has facilitated the interpretation of the underlying molecular mechanisms for complex human diseases at the modular level.

Ti-based bulk metallic glass composites (BMGMCs) containing an in situ formed metastable β phase normally exhibit enhanced plasticity attributed to induced phase transformation or twinning. However, the underlying deformation micromechanism remains controversial. This study investigates a novel deformation mechanism of Ti-based BMGMCs with a composition of Ti42.3Zr28Cu8.3Nb4.7Ni1.7Be15 (at%). The microstructures after tension were analyzed using advanced electron microscopy. The dendrites were homogeneously distributed in the glassy matrix with a volume fraction of 55 ± 2% and a size of 1~5 μm. The BMGMCs deformed in a serrated manner with a fracture strength (σf) of ~1710 MPa and a fracture strain of ~7.1%, accompanying strain hardening. The plastic deformation beyond yielding was achieved by a synergistic action, which includes shear banding, localized amorphization and a localized BCC (β-Ti) to HCP (α-Ti) structural transition. The localized amorphization was caused by high local strain rates during shear band extension from the amorphous matrix to the crystalline reinforcements. The localized structural transition from BCC to HCP resulted from accumulating concentrated stress during deformation. The synergistic action enriches our understanding of the deformation mechanism of Ti-based BMGMCs and also sheds light on material design and performance improvement. [ABSTRACT FROM AUTHOR]

Cell–cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring intercellular communications from single-cell RNA sequencing (scRNA-seq) data lack a comprehensive global network view of multilayered communications. In this context, we propose scHyper, a new method that can infer intercellular communications from a global network perspective and identify the potential impact of all cells, ligand, and receptor expression on the communication score. scHyper designed a new way to represent tripartite relationships, by extracting a heterogeneous hypergraph that includes the source (ligand expression), the target (receptor expression), and the relevant ligand–receptor (L-R) pairs. scHyper is based on hypergraph representation learning, which measures the degree of match between the intrinsic attributes (static embeddings) of nodes and their observed behaviors (dynamic embeddings) in the context (hyperedges), quantifies the probability of forming hyperedges, and thus reconstructs the cell–cell communication score. Additionally, to effectively mine the key mechanisms of signal transmission, we collect a rich dataset of multisubunit complex L-R pairs and propose a nonparametric test to determine significant intercellular communications. Comparing with other tools indicates that scHyper exhibits superior performance and functionality. Experimental results on the human tumor microenvironment and immune cells demonstrate that scHyper offers reliable and unique capabilities for analyzing intercellular communication networks. Therefore, we introduced an effective strategy that can build high-order interaction patterns, surpassing the limitations of most methods that can only handle low-order interactions, thus more accurately interpreting the complexity of intercellular communications. [ABSTRACT FROM AUTHOR]