Infection mechanism of Botryosphaeria dothidea and the disease resistance strategies of Chinese hickory (Carya cathayensis).

Botryosphaeria dothidea is the main fungal pathogen responsible for causing Chinese hickory (Carya cathayensis) dry rot disease, posing a serious threat to the Chinese hickory industry. Understanding the molecular basis of B. dothidea infection and the host's resistance mechanisms is crucial for controlling and managing the ecological impact of Chinese hickory dry rot disease. This study utilized ultrastructural observations to reveal the process of B. dothidea infection and colonization in Chinese hickory, and investigated the impact of B. dothidea infection on Chinese hickory biochemical indicators and plant hormone levels. Through high-throughput transcriptome sequencing, the gene expression profiles associated with different stages of B. dothidea infection in Chinese hickory and their corresponding defense responses were described. Additionally, a series of key genes closely related to non-structural carbohydrate metabolism, hormone metabolism, and plant-pathogen interactions during B. dothidea infection in Chinese hickory were identified, including genes encoding DUF, Myb_DNA-binding, and ABC transporter proteins. These findings provide important insights into elucidating the pathogenic mechanisms of B. dothidea and the resistance genes in Chinese hickory.

Water and Fertilizer Management Is an Important Way to Synergistically Enhance the Yield, Rice Quality and Lodging Resistance of Hybrid Rice.

This study comprehensively investigated the synergistic effects and underlying mechanisms of optimized water and fertilizer management on the yield, quality, and lodging resistance of hybrid rice (Oryza sativa), through a two-year field experiment. Two hybrid rice varieties, Xinxiangliangyou 1751 (XXLY1751) and Yueliangyou Meixiang Xinzhan (YLYMXXZ), were subjected to three irrigation methods (W1: wet irrigation, W2: flooding irrigation, W3: shallow-wet-dry irrigation) and four nitrogen fertilizer treatments (F1 to F4 with application rates of 0, 180, 225, and 270 kg ha-1, respectively). Our results revealed that the W1F3 treatment significantly enhanced photosynthetic efficiency and non-structural carbohydrate (NSC) accumulation, laying a robust foundation for high yield and quality. NSC accumulation not only supported rice growth but also directly influenced starch and protein synthesis, ensuring smooth grain filling and significantly improving yield and quality. Moreover, NSC strengthened stem fullness and thickness, converting them into structural carbohydrates such as cellulose and lignin, which substantially increased stem mechanical strength and lodging resistance. Statistical analysis demonstrated that water and fertilizer treatments had significant main and interactive effects on photosynthetic rate, dry matter accumulation, yield, quality parameters, NSC, cellulose, lignin, and stem bending resistance. This study reveals the intricate relationship between water and fertilizer management and NSC dynamics, providing valuable theoretical and practical insights for high-yield and high-quality cultivation of hybrid rice, significantly contributing to the sustainable development of modern agriculture.

Innovative prognostication: a novel nomogram for post-interventional aneurysmal subarachnoid hemorrhage patients.

Background and purpose: Spontaneous aneurysmal subarachnoid hemorrhage (aSAH) is a common acute cerebrovascular disease characterized by severe illness, high mortality, and potential cognitive and motor impairments. We carried out a retrospective study at Fujian Provincial Hospital to establish and validate a model for forecasting functional outcomes at 6 months in aSAH patients who underwent interventional embolization. Methods: 386 aSAH patients who underwent interventional embolization between May 2012 and April 2022 were included in the study. We established a logistic regression model based on independent risk factors associated with 6-month adverse outcomes (modified Rankin Scale Score ≥ 3, mRS). We evaluated the model's performance based on its discrimination, calibration, clinical applicability, and generalization ability. Finally, the study-derived prediction model was also compared with other aSAH prognostic scales and the model's itself constituent variables to assess their respective predictive efficacy. Results: The predictors considered in our study were age, the World Federation of Neurosurgical Societies (WFNS) grade of IV-V, mFisher score of 3-4, secondary cerebral infarction, and first leukocyte counts on admission. Our model demonstrated excellent discrimination in both the modeling and validation cohorts, with an area under the curve of 0.914 (p < 0.001, 95%CI = 0.873-0.956) and 0.947 (p < 0.001, 95%CI = 0.907-0.987), respectively. Additionally, the model also exhibited good calibration (Hosmer-Lemeshow goodness-of-fit test: X2 = 9.176, p = 0.328). The clinical decision curve analysis and clinical impact curve showed favorable clinical applicability. In comparison to other prediction models and variables, our model displayed superior predictive performance. Conclusion: The new prediction nomogram has the capability to forecast the unfavorable outcomes at 6 months after intervention in patients with aSAH.

The value of computed tomography angiography for evaluation of left atrial enlargement in patients with persistent atrial fibrillation.

BACKGROUND: The post-processing technology of CTA offers significant advantages in evaluating left atrial enlargement (LAE) in patients with persistent atrial fibrillation (PAF). This study aims to identify parameters for rapidly and accurately diagnosing LAE in patients with PAF using CT cross-sections. METHODS: Left atrial pulmonary venous (PV) CT was performed to 300 PAF patients with dual-source CT, and left atrial volume (LAV), left atrial anteroposterior diameter (LAD1), left atrial transverse diameter (LAD2), and left atrial area (LAA) were measured in the ventricular end systolic (ES) and middle diastolic (MD). LA index (LAI) = LA parameter/body surface area (BSA). Left atrial volume index (LAVIES) > 77.7 ml/m2 was used as the reference standard for the LAE diagnosis. RESULTS: 227 patients were enrolled in the group, 101 (44.5%) of whom had LAE. LAVES and LAVMD (r = 0.983), LAVIES and LAVIMD (r = 0.984), LAAES and LAVIES (r = 0.817), LAAMD and LAVIES (r = 0.814) had strong positive correlations. The area under curve (AUC) showed that all measured parameters were suitable for diagnosing LAE, and the diagnostic efficacy was compared as follows: LAA/LAAI> LAD> the relative value index of LAD, LAD2> LAD1. LAA and LAAI demonstrated comparable diagnostic efficacy, with LAA being more readily available than LAAI. CONCLUSIONS: The axial LAA measured by CTA can be served as a parameter for the rapid and accurate diagnosis of LAE in patients with PAF.

An interfacial host-guest inclusion complex regulated supramolecular nanocomposite hydrogel showing tunable mechanical strength, self-healing, strain sensitivity and NIR responsiveness.

The development of supramolecular nanocomposite hydrogels with good mechanical properties and multifunctional characteristics remains challenging. The reinforced role of interfacial weak interactions is important for the mechanical properties of nanocomposite hydrogels. Here, a dynamic host-guest inclusion complex from the host molecule CB[7] and guest units was employed to prepare Fe3O4 hybrid supramolecular nanocomposite hydrogels. The results show that the as-obtained hydrogel with a porous structure was prepared. The CB[7]-modified Fe3O4 (Fe3O4@CB[7]) nanoparticles severed as a cross-linker for fabricating the hydrogel's network. By changing the Fe3O4@CB[7] content, their tensile stress ranged from 0.102 to 0.403 MPa and their compression stress ranged (70% compression strain) from 0.059 to 0.775 MPa. By changing the guest units, their tensile stress ranged from 0.3 MPa to 0.403 MPa. The self-healing efficiency of the hydrogels was 99% after 48 h at room temperature. The as-obtained hydrogels with strain sensitivity can be applied for detecting the movement of an elbow and finger. The supramolecular hydrogel exhibits NIR responsiveness, self-healing, injectability, tunable mechanical strength and conductive ability, and can be used in flexible electronics.

Recent advances and applications of artificial intelligence in 3D bioprinting.

3D bioprinting techniques enable the precise deposition of living cells, biomaterials, and biomolecules, emerging as a promising approach for engineering functional tissues and organs. Meanwhile, recent advances in 3D bioprinting enable researchers to build in vitro models with finely controlled and complex micro-architecture for drug screening and disease modeling. Recently, artificial intelligence (AI) has been applied to different stages of 3D bioprinting, including medical image reconstruction, bioink selection, and printing process, with both classical AI and machine learning approaches. The ability of AI to handle complex datasets, make complex computations, learn from past experiences, and optimize processes dynamically makes it an invaluable tool in advancing 3D bioprinting. The review highlights the current integration of AI in 3D bioprinting and discusses future approaches to harness the synergistic capabilities of 3D bioprinting and AI for developing personalized tissues and organs.

Discovery of Novel Acethydrazide-Containing Flavonol Derivatives as Potential Antifungal Agents.

In this study, a series of novel hydrazide-containing flavonol derivatives was designed, synthesized, and evaluated for antifungal activity. In the in vitro antifungal assay, most of the target compounds exhibited potent antifungal activity against seven tested phytopathogenic fungi. In particular, compound C32 showed the best antifungal activity against Rhizoctonia solani (EC50 = 0.170 µg/mL), outperforming carbendazim (EC50 = 0.360 µg/mL) and boscalid (EC50 = 1.36 µg/mL). Compound C24 exhibited excellent antifungal activity against Valsa mali, Botrytis cinerea, and Alternaria alternata with EC50 values of 0.590, 0.870, and 1.71 µg/mL, respectively. The in vivo experiments revealed that compounds C32 and C24 were potential novel agricultural antifungals. 3D quantitative structure-activity relationship (3D-QSAR) models were used to analyze the structure-activity relationships of these compounds. The analysis results indicated that introducing appropriate electronegative groups at position 4 of a benzene ring could effectively improve the anti-R. solani activity. In the antifungal mechanism study, scanning electron microscopy and transmission electron microscopy analyses revealed that C32 disrupted the normal growth of hyphae by affecting the structural integrity of the cell membrane and cellular respiration. Furthermore, compound C32 exhibited potent succinate dehydrogenase (SDH) inhibitory activity (IC50 = 8.42 µM), surpassing that of the SDH fungicide boscalid (IC50 = 15.6 µM). The molecular dynamics simulations and docking experiments suggested that compound C32 can occupy the active site and form strong interactions with the key residues of SDH. Our findings have great potential for aiding future research on plant disease control in agriculture.

Single-cell transcriptomics across 2,534 microbial species reveals functional heterogeneity in the rumen microbiome.

Deciphering the activity of individual microbes within complex communities and environments remains a challenge. Here we describe the development of microbiome single-cell transcriptomics using droplet-based single-cell RNA sequencing and pangenome-based computational analysis to characterize the functional heterogeneity of the rumen microbiome. We generated a microbial genome database (the Bovine Gastro Microbial Genome Map) as a functional reference map for the construction of a single-cell transcriptomic atlas of the rumen microbiome. The atlas includes 174,531 microbial cells and 2,534 species, of which 172 are core active species grouped into 12 functional clusters. We detected single-cell-level functional roles, including a key role for Basfia succiniciproducens in the carbohydrate metabolic niche of the rumen microbiome. Furthermore, we explored functional heterogeneity and reveal metabolic niche trajectories driven by biofilm formation pathway genes within B. succiniciproducens. Our results provide a resource for studying the rumen microbiome and illustrate the diverse functions of individual microbial cells that drive their ecological niche stability or adaptation within the ecosystem.

An automated hybrid approach via deep learning and radiomics focused on the midbrain and substantia nigra to detect early-stage Parkinson's disease.

Objectives: The altered neuromelanin in substantia nigra pars compacta (SNpc) is a valuable biomarker in the detection of early-stage Parkinson's disease (EPD). Diagnosis via visual inspection or single radiomics based method is challenging. Thus, we proposed a novel hybrid model that integrates radiomics and deep learning methodologies to automatically detect EPD based on neuromelanin-sensitive MRI, namely short-echo-time Magnitude (setMag) reconstructed from quantitative susceptibility mapping (QSM). Methods: In our study, we collected QSM images including 73 EPD patients and 65 healthy controls, which were stratified into training-validation and independent test sets with an 8:2 ratio. Twenty-four participants from another center were included as the external validation set. Our framework began with the detection of the brainstem utilizing YOLO-v5. Subsequently, a modified LeNet was applied to obtain deep learning features. Meanwhile, 1781 radiomics features were extracted, and 10 features were retained after filtering. Finally, the classified models based on radiomics features, deep learning features, and the hybrid of both were established through machine learning algorithms, respectively. The performance was mainly evaluated using accuracy, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). The saliency map was used to visualize the model. Results: The hybrid feature-based support vector machine (SVM) model showed the best performance, achieving ACC of 96.3 and 95.8% in the independent test set and external validation set, respectively. The model established by hybrid features outperformed the one radiomics feature-based (NRI: 0.245, IDI: 0.112). Furthermore, the saliency map showed that the bilateral "swallow tail" sign region was significant for classification. Conclusion: The integration of deep learning and radiomic features presents a potent strategy for the computer-aided diagnosis of EPD. This study not only validates the accuracy of our proposed model but also underscores its interpretability, evidenced by differential significance across various anatomical sites.

Manipulating Local Chemistry and Coherent Structures for High-Rate and Long-Life Sodium-Ion Battery Cathodes.

Layered sodium transition-metal (TM) oxides generally suffer from severe capacity decay and poor rate performance during cycling, especially at a high state of charge (SoC). Herein, an insight into failure mechanisms within high-voltage layered cathodes is unveiled, while a two-in-one tactic of charge localization and coherent structures is devised to improve structural integrity and Na+ transport kinetics, elucidated by density functional theory calculations. Elevated Jahn-Teller [Mn3+O6] concentration on the particle surface during sodiation, coupled with intense interlayer repulsion and adverse oxygen instability, leads to irreversible damage to the near-surface structure, as demonstrated by X-ray absorption spectroscopy and in situ characterization techniques. It is further validated that the structural skeleton is substantially strengthened through the electronic structure modulation surrounding oxygen. Furthermore, optimized Na+ diffusion is effectively attainable via regulating intergrown structures, successfully achieved by the Zn2+ inducer. Greatly, good redox reversibility with an initial Coulombic efficiency of 92.6%, impressive rate capability (86.5 mAh g-1 with 70.4% retention at 10C), and enhanced cycling stability (71.6% retention after 300 cycles at 5C) are exhibited in the P2/O3 biphasic cathode. It is believed that a profound comprehension of layered oxides will herald fresh perspectives to develop high-voltage cathode materials for sodium-ion batteries.

A conserved N-terminal motif of CUL3 contributes to assembly and E3 ligase activity of CRL3KLHL22.

The CUL3-RING E3 ubiquitin ligases (CRL3s) play an essential role in response to extracellular nutrition and stress stimuli. The ubiquitin ligase function of CRL3s is activated through dimerization. However, how and why such a dimeric assembly is required for its ligase activity remains elusive. Here, we report the cryo-EM structure of the dimeric CRL3KLHL22 complex and reveal a conserved N-terminal motif in CUL3 that contributes to the dimerization assembly and the E3 ligase activity of CRL3KLHL22. We show that deletion of the CUL3 N-terminal motif impairs dimeric assembly and the E3 ligase activity of both CRL3KLHL22 and several other CRL3s. In addition, we found that the dynamics of dimeric assembly of CRL3KLHL22 generates a variable ubiquitination zone, potentially facilitating substrate recognition and ubiquitination. These findings demonstrate that a CUL3 N-terminal motif participates in the assembly process and provide insights into the assembly and activation of CRL3s.

Integrated transcriptomic and lipidomic analysis provides key insights into lipid content changes during pecan (Carya illinoensis) fruit development.

Pecans [Carya illinoinensis (Wangenh.) K. Koch] are highly valued for their abundance of quality healthy lipids, positively impacting human health and making themselves a preferred choice for nutritionally rich foods. However, a comprehensive understanding of the high-resolution characteristics of pecan fruit lipid composition and its dynamic changes, as well as the transfer between embryo and pericarp during development, remains incomplete. In this study, through integrated multi-omics analysis, we observed significant spatiotemporal heterogeneity in lipid changes between the pericarp and embryo. It showed smaller fluctuations and more stable lipid levels in the pericarp while exhibiting a dynamic pattern of initially increasing and then decreasing lipid content in the embryo. In this study, a total of 52 differentially expressed genes were identified, related to fatty acid synthesis and metabolism pathways in the two tissues, with changes in oleic acid and linoleic acid composition being the primary features of the embryo. This research lays the foundation for further understanding the differential regulation mechanisms of lipid metabolism between embryo and pericarp. Overall, this study filled the knowledge gap regarding dynamic changes in pericarp lipid metabolites, provided crucial insights into the lipid metabolism network during pecan fruit development, and established a scientific basis for the genetic improvement of pecan crops.

Reconciling the Cooperative-Competitive Patterns among Tumor and Immune Cells for Triple-Negative Breast Cancer Treatment Using Multimodule Nanocomplexes.

Targeting the competitive-cooperative relationships among tumor cells and various immune cells can efficiently reverse the immune-dysfunction microenvironment to boost the immunotherapies for the triple-negative breast cancer treatment. Hence, a bacterial outer membrane vesicle-based nanocomplex is designed for specifically targeting malignant cells and immune cells to reconcile the relationships based on metabolic-immune crosstalk. By uniquely utilizing the property of charge-reversal polymers to realize function separation, the nanocomplexes could synergistically regulate tumor cells and immune cells. This approach could reshape the immunosuppressive competition-cooperation pattern into one that is immune-responsive, showcasing significant potential for inducing tumor remission in TNBC models.

Raman Flow Cytometry and Its Biomedical Applications.

Raman flow cytometry (RFC) uniquely integrates the "label-free" capability of Raman spectroscopy with the "high-throughput" attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.

Fully inkjet-printed Ag2Se flexible thermoelectric devices for sustainable power generation.

Flexible thermoelectric devices show great promise as sustainable power units for the exponentially increasing self-powered wearable electronics and ultra-widely distributed wireless sensor networks. While exciting proof-of-concept demonstrations have been reported, their large-scale implementation is impeded by unsatisfactory device performance and costly device fabrication techniques. Here, we develop Ag2Se-based thermoelectric films and flexible devices via inkjet printing. Large-area patterned arrays with microscale resolution are obtained in a dimensionally controlled manner by manipulating ink formulations and tuning printing parameters. Printed Ag2Se-based films exhibit (00 l)-textured feature, and an exceptional power factor (1097 µWm-1K-2 at 377 K) is obtained by engineering the film composition and microstructure. Benefiting from high-resolution device integration, fully inkjet-printed Ag2Se-based flexible devices achieve a record-high normalized power (2 µWK-2cm-2) and superior flexibility. Diverse application scenarios are offered by inkjet-printed devices, such as continuous power generation by harvesting thermal energy from the environment or human bodies. Our strategy demonstrates the potential to revolutionize the design and manufacture of multi-scale and complex flexible thermoelectric devices while reducing costs, enabling them to be integrated into emerging electronic systems as sustainable power sources.

Angiosperm-wide analysis of fruit and ovary evolution aided by a new nuclear phylogeny supports association of the same ovary type with both dry and fleshy fruits.

Fruit functions in seed protection and dispersal and belongs to many dry and fleshy types, yet their evolutionary pattern remains unclear in part due to uncertainties in the phylogenetic relationships among several orders and families. Thus we used nuclear genes of 502 angiosperm species representing 231 families to reconstruct a well supported phylogeny, with resolved relationships for orders and families with previously uncertain placements. Using this phylogeny as a framework, molecular dating supports a Triassic origin of the crown angiosperms, followed by the emergence of most orders in the Jurassic and Cretaceous and their rise to ecological dominance during the Cretaceous Terrestrial Revolution. The robust phylogeny allowed an examination of the evolutionary pattern of fruit and ovary types, revealing a trend of parallel carpel fusions during early diversifications in eudicots, monocots, and magnoliids. Moreover, taxa in the same order or family with the same ovary type can develop either dry or fleshy fruits with strong correlations between specific types of dry and fleshy fruits; such associations of ovary, dry and fleshy fruits define several ovary-fruit "modules" each found in multiple families. One of the frequent modules has an ovary containing multiple ovules, capsules and berries, and another with an ovary having one or two ovules, achenes (or other single-seeded dry fruits) and drupes. This new perspective of relationships among fruit types highlights the closeness of specific dry and fleshy fruit types, such as capsule and berry, that develop from the same ovary type and belong to the same module relative to dry and fleshy fruits of other modules (such as achenes and drupes). Further analyses of gene families containing known genes for ovary and fruit development identified phylogenetic nodes with multiple gene duplications, supporting a possible role of whole-genome duplications, in combination with climate changes and animal behaviors, in angiosperm fruit and ovary diversification.

Automatic segmentation of hepatocellular carcinoma on dynamic contrast-enhanced MRI based on deep learning.

Objective. Precise hepatocellular carcinoma (HCC) detection is crucial for clinical management. While studies focus on computed tomography-based automatic algorithms, there is a rareness of research on automatic detection based on dynamic contrast enhanced (DCE) magnetic resonance imaging. This study is to develop an automatic detection and segmentation deep learning model for HCC using DCE.Approach: DCE images acquired from 2016 to 2021 were retrospectively collected. Then, 382 patients (301 male; 81 female) with 466 lesions pathologically confirmed were included and divided into an 80% training-validation set and a 20% independent test set. For external validation, 51 patients (42 male; 9 female) in another hospital from 2018 to 2021 were included. The U-net architecture was modified to accommodate multi-phasic DCE input. The model was trained with the training-validation set using five-fold cross-validation, and furtherly evaluated with the independent test set using comprehensive metrics for segmentation and detection performance. The proposed automatic segmentation model consisted of five main steps: phase registration, automatic liver region extraction using a pre-trained model, automatic HCC lesion segmentation using the multi-phasic deep learning model, ensemble of five-fold predictions, and post-processing using connected component analysis to enhance the performance to refine predictions and eliminate false positives.Main results. The proposed model achieved a mean dice similarity coefficient (DSC) of 0.81 ± 0.11, a sensitivity of 94.41 ± 15.50%, a precision of 94.19 ± 17.32%, and 0.14 ± 0.48 false positive lesions per patient in the independent test set. The model detected 88% (80/91) HCC lesions in the condition of DSC > 0.5, and the DSC per tumor was 0.80 ± 0.13. In the external set, the model detected 92% (58/62) lesions with 0.12 ± 0.33 false positives per patient, and the DSC per tumor was 0.75 ± 0.10.Significance.This study developed an automatic detection and segmentation deep learning model for HCC using DCE, which yielded promising post-processed results in accurately identifying and delineating HCC lesions.

Structural and functional insights into the lipid regulation of human anion exchanger 2.

Anion exchanger 2 (AE2) is an electroneutral Na+-independent Cl-/HCO3- exchanger belongs to the SLC4 transporter family. The widely expressed AE2 participates in a variety of physiological processes, including transepithelial acid-base secretion and osteoclastogenesis. Both the transmembrane domains (TMDs) and the N-terminal cytoplasmic domain (NTD) are involved in regulation of AE2 activity. However, the regulatory mechanism remains unclear. Here, we report a 3.2 Å cryo-EM structure of the AE2 TMDs in complex with PIP2 and a 3.3 Å full-length mutant AE2 structure in the resting state without PIP2. We demonstrate that PIP2 at the TMD dimer interface is involved in the substrate exchange process. Mutation in the PIP2 binding site leads to the displacement of TM7 and further stabilizes the interaction between the TMD and the NTD. Reduced substrate transport activity and conformation similar to AE2 in acidic pH indicating the central contribution of PIP2 to the function of AE2.

Altered whole-brain functional network in patients with frontal low-grade gliomas: a resting-state functional MRI study.

PURPOSE: Gliomas are the most common primary brain tumor. Currently, topological alterations of whole-brain functional network caused by gliomas are not fully understood. The work here clarified the topological reorganization of the functional network in patients with unilateral frontal low-grade gliomas (LGGs). METHODS: A total of 45 patients with left frontal LGGs, 19 with right frontal LGGs, and 25 healthy controls (HCs) were enrolled. All the resting-state functional MRI (rs-fMRI) images of the subjects were preprocessed to construct the functional network matrix, which was used for graph theoretical analysis. A two-sample t-test was conducted to clarify the differences in global and nodal network metrics between patients and HCs. A network-based statistic approach was used to identify the altered specific pairs of regions in which functional connectivity in patients with LGGs. RESULTS: The local efficiency, clustering coefficient, characteristic path length, and normalized characteristic path length of patients with unilateral frontal LGGs were significantly lower than HCs, while there were no significant differences of global efficiency and small-worldness between patients and HCs. Compared with the HCs, betweenness centrality, degree centrality, and nodal efficiency of several brain nodes were changed significantly in patients. Around the tumor and its adjacent areas, the inter- and intra-hemispheric connections were significantly decreased in patients with left frontal LGGs. CONCLUSION: The patients with unilateral frontal LGGs have altered global and nodal network metrics and decreased inter- and intra-hemispheric connectivity. These topological alterations may be involved in functional impairment and compensation of patients.

Modeling critical thermoelectric transports driven by band broadening and phonon softening.

Critical phenomena are one of the most captivating areas of modern physics, whereas the relevant experimental and theoretical studies are still very challenging. Particularly, the underlying mechanism behind the anomalous thermoelectric properties during critical phase transitions remains elusive, i.e., the current theoretical models for critical electrical transports are either qualitative or solely focused on a specific transport parameter. Herein, we develop a quantitative theory to model the electrical transports during critical phase transitions by incorporating both the band broadening effect and carrier-soft TO phonon interactions. It is found that the band-broadening effect contributes an additional term to Seebeck coefficient, while the carrier-soft TO phonon interactions greatly affects both electrical resistivity and Seebeck coefficient. The universality and validity of our model are well confirmed by experimental data. Furthermore, the features of critical phase transitions are effectively tuned. For example, alloying S in Cu2Se can reduce the phase transition temperature but increase the phase transition parameter b. The maximum thermoelectric figure of merit zT is pushed to a high value of 1.3 at the critical point (377 K), which is at least twice as large as those of normal static phases. This work not only provides a clear picture of the critical electrical transports but also presents new guidelines for future studies in this exciting area.