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Background: Sanniang Bay (SNB) and Dafeng River (DFR), located in the northern Beibu Gulf, is well-known as one of the eight habitats for humpback dolphins in China. This region is representative of typical estuarine and bay ecosystems and produce complex hydrodynamic seawater conditions. Moreover, anthropogenic pressure, such as eutrophication and large-scale infrastructure projects, have caused ongoing habitat deterioration and loss. It is urgent to know the phytoplankton community and their relationships with environmental factors in this region., Results: In this study, we assessed the diversity and assembly mechanisms of phytoplankton communities, as well as their relationship with the physicochemical characteristics of seawater in SNB and DFR region using 18 S rDNA metabarcoding analysis. The results showed that seasonal changes markedly impacted the alpha diversity of the phytoplankton community. From March to July, with the average temperature increasing from 25.2℃ to 28.1℃,the Shannon or Species Richness were negatively correlated with temperature. During hot season (in Sep, average temperature 32.1℃), phytoplankton diversity was negatively correlated with nutrients (NH 4  + , NO 3 - , PO 4 3- , TN). Additionally, during the rainy season, the Bray-Curtis similarity of the phytoplankton community was significantly lower than during the dry season. In March, the distance among the sampling sites was most strongly and positively correlated with the Bray-Curtis dissimilarity. Stochastic processes, specifically dispersal limitation and ecological drift, are the primary drivers of community assembly, while deterministic assembly processes (mainly heterogeneous selection) contribute a relatively minor portion (< 17%)., Conclusions: Rising temperature diminished the diversity of phytoplankton in SNB and DFR, and nutrient inputs and eutrophication in estuarine areas will aggravate the loss of phytoplankton diversity., (© 2024. The Author(s).)

The incidence of multiple primary lung cancer (MPLC) is increasing, with some of our surgical patients exhibiting numerous lesions. We defined lung cancer with five or more primary lesions as super MPLCs. Elucidating the genomic characteristics of this special MPLC subtype can help reduce disease burden and understand tumor evolution. In our cohort of synchronous super early-stage MPLCs (PUMCH-ssesMPLC), whole-exome sequencing on 130 resected malignant specimens from 18 patients provided comprehensive super-MPLC genomic landscapes. Mutations are enriched in PI3k-Akt and MAPK pathways. Their BRAF mutation frequency (31.5%) is significantly higher than MPLC with fewer lesions and early-stage single-lesion cancer, while EGFR mutations are significantly fewer (13.8%). As lesion counts increase, BRAF mutations gradually become dominant. Also, invasive lesions more tend to have classic super-MPLC mutation patterns. High-frequency BRAF mutations, especially Class II, and low-frequency EGFR mutations could be a reason for the limited effectiveness of targeted therapy in super-MPLC patients., (© 2024. The Author(s).)

The interleukin 17 (IL-17) family of cytokines has emerged as a critical player in autoimmune disease, including systemic lupus erythematosus (SLE). However, the role of IL-17B, a poorly understood cytokine, in the pathogenesis of SLE is still not known. In this study, we investigated the role of IL-17B in the activation and differentiation of B cells, and the pathogenesis of SLE. Intriguingly, IL-17B deficiency aggravated disease in lupus-prone mice and promoted the activation of B cells and the differentiation of germinal center B cells and plasma cells, while recombinant mouse IL-17B (rmIL-17B) significantly alleviated disease in lupus-prone mice. Mechanistically, rmIL-17B inhibited the activation of the Toll-like receptor and interferon pathways in B cells by downregulating fatty acid synthase-mediated (FASN-mediated) lipid metabolism. Loss of FASN significantly alleviated the disease in lupus-prone mice and inhibited the activation and differentiation of B cells. In addition, B cells had greater FASN expression and lower IL-17RB levels in patients with SLE than in healthy controls. Our study describes the role of IL-17B in regulating B cell activation and differentiation, and alleviating the onset of SLE. These findings will lay a theoretical foundation for further understanding of the pathogenesis of SLE.

Disentangling how climate oscillations and geographical events significantly influence plants' genetic architecture and demographic history is a central topic in phytogeography. The deciduous ancient tree species Ulmus macrocarpa is primarily distributed throughout Northern China and has timber and horticultural value. In the current study, we studied the phylogenic architecture and demographical history of U. macrocarpa using chloroplast DNA with ecological niche modeling. The results indicated that the populations' genetic differentiation coefficient ( N ST ) value was significantly greater than the haplotype frequency ( G ST ) ( p < 0.05), suggesting that U. macrocarpa had a clear phylogeographical structure. Phylogenetic inference showed that the putative chloroplast haplotypes could be divided into three groups, in which the group Ⅰ was considered to be ancestral. Despite significant genetic differentiation among these groups, gene flow was detected. The common ancestor of all haplotypes was inferred to originate in the middle-late Miocene, followed by the haplotype overwhelming diversification that occurred in the Quaternary. Combined with demography pattern and ecological niche modeling, we speculated that the surrounding areas of Shanxi and Inner Mongolia were potential refugia for U. macrocarpa during the glacial period in Northern China. Our results illuminated the demography pattern of U. macrocarpa and provided clues and references for further population genetics investigations of precious tree species distributed in Northern China.

This study focused on developing a Pickering emulsion fresh-keeping paper that contained clove essential oil (CEO). Cherry tomatoes served as the test material for assessing the preservative efficacy of fresh-keeping paper. The results showed that Pickering emulsion had strong stability. Additionally, the fresh-keeping paper had a good antioxidant activity and sustained-release effect on CEO. In terms of the preservation effect, 0.75 wt% CEO Pickering emulsion paper reduced the decay incidence and weight loss of cherry tomatoes during 12-day storage. Fresh-keeping paper could also play a positive role in protecting the sensory index and color difference of tomatoes. It slowed the decline rate of soluble solid concentration (SSC) and titrable acid (TA). The vitamin C (Vc) and hardness of preserved tomatoes using fresh-keeping paper were maintained at a high level. The paper also inhibited the growth of microorganisms significantly. Therefore, 0.75 wt% CEO Pickering emulsion fresh-keeping paper displayed considerable potential for application in the preservation of postharvest fruits and vegetables. It is a novel fruit and vegetable preservation material worthy of development.

The increase in the detection rate of synchronous multiple primary lung cancer (MPLC) has posed remarkable clinical challenges due to the limited understanding of its pathogenesis and molecular features. Here, comprehensive comparisons of genomic and immunologic features between MPLC and solitary lung cancer nodule (SN), as well as different lesions of the same patient, were performed. Compared with SN, MPLC displayed a lower rate of EGFR mutation but higher rates of BRAF, MAP2K1, and MTOR mutation, which function exactly in the upstream and downstream of the same signaling pathway. Considerable heterogeneity in T cell receptor (TCR) repertoire exists among not only different patients but also among different lesions of the same patient. Invasive lesions of MPLC exhibited significantly higher TCR diversity and lower TCR expansion than those of SN. Intriguingly, different lesions of the same patient always shared a certain proportion of TCR clonotypes. Significant clonal expansion could be observed in shared TCR clonotypes, particularly in those existing in all lesions of the same patient. In conclusion, this study provided evidences of the distinctive mutational landscape, activation of oncogenic signaling pathways, and TCR repertoire in MPLC as compared with SN. The significant clonal expansion of shared TCR clonotypes demonstrated the existence of immune commonality among different lesions of the same patient and shed new light on the individually tailored precision therapy for MPLC., (© 2024. The Author(s).)

3D printing has exhibited significant potential in outer space and medical implants. To use this technology in the specific high-value scenarios, 3D-printed parts need to satisfy quality-related requirements. In this article, the influence of the filament feeder operating states of 3D printer on the compressive properties of 3D-printed parts is studied in the fused filament fabrication process. A machine learning approach, back-propagation neural network with a genetic algorithm (GA-BPNN) optimized by k -fold cross-validation, is proposed to monitor the operating states and predict the compressive properties. Vibration and current sensors are used in situ to monitor the operating states of the filament feeder, and a set of features are extracted and selected from raw sensor data in time and frequency domains. Results show that the operating states of the filament feeder significantly affected the compressive properties of the fabricated samples, the operating states were accurately recognized with 96.3% rate, and compressive properties were successfully predicted by the GA-BPNN. This proposed method has the potential for use in industrial applications after 3D printing without requiring any further quality control., Competing Interests: No competing financial interests exist., (Copyright 2023, Mary Ann Liebert, Inc., publishers.)

Minute pulmonary meningothelial-like nodules (MPMNs) are benign small lesions in the lungs, with similar pathological characteristics to the meningeal epithelium. MPMNs have similar imaging manifestations to malignant tumors, which can lead to misdiagnosis in clinical practice. There is no consensus on the pathogenesis of MPMNs, with some suggest that MPMNs derive from reactive proliferation, while others suggest that MPMNs share a common origin and molecular mechanism with meningiomas in the central nervous system. Understanding the characteristics of MPMNs and studying their pathogenesis will help improve the understanding and diagnosis of MPMNs. In this article, we reviewed the clinical, pathological, imaging characteristics, differential diagnosis and pathogenesis of MPMNs. We also analyze the existing research advances regarding the pathogenesis and propose prospects for further research.
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Ultrathin superconducting films are the basis of superconductor devices. van der Waals (vdW) NbSe 2 with noncentrosymmetry exhibits exotic superconductivity and shows promise in superconductor electronic devices. However, the growth of inch-scale NbSe 2 films with layer regulation remains a challenge because vdW structural material growth is strongly dependent on the epitaxial guidance of the substrate. Herein, a vdW self-epitaxy strategy is developed to eliminate the substrate driving force in film growth and realize inch-sized NbSe 2 film growth with thicknesses from 2.1 to 12.1 nm on arbitrary substrates. The superconducting transition temperature of 5.1 K and superconducting transition width of 0.30 K prove the top homogeneity and quality of superconductivity among all of the synthetic NbSe 2 films. Coupled with a large area and substrate compatibility, this work paves the way for developing NbSe 2 superconductor electronics.

Superconducting nanocircuits, which are usually fabricated from superconductor films, are the core of superconducting electronic devices. While emerging transition-metal dichalcogenide superconductors (TMDSCs) with exotic properties show promise for exploiting new superconducting mechanisms and applications, their environmental instability leads to a substantial challenge for the nondestructive preparation of TMDSC nanocircuits. Here, we report a universal strategy to fabricate TMDSC nanopatterns via a topotactic conversion method using prepatterned metals as precursors. Typically, robust NbSe 2 meandering nanowires can be controllably manufactured on a wafer scale, by which a superconducting nanowire circuit is principally demonstrated toward potential single photon detection. Moreover, versatile superconducting nanocircuits, e.g., periodical circle/triangle hole arrays and spiral nanowires, can be prepared with selected TMD materials (NbS 2 , TiSe 2 , or MoTe 2 ). This work provides a generic approach for fabricating nondestructive TMDSC nanocircuits with precise control, which paves the way for the application of TMDSCs in future electronics., (© 2023. The Author(s).)

Poria cocos alkali-soluble polysaccharide (PCAP), a water-insoluble β-glucan, is the main component of the total dried sclerotia of Poria cocos. However, its gelation behaviour and properties have yet to be comprehensively studied. In this study, an acid-induced physical hydrogel based on natural PCAP is fabricated. The acid-induced gelation in PCAP is explored with respect to the pH and polysaccharide concentration. PCAP hydrogels are formed in the pH range of 0.3-10.5, and the lowest gelation concentration is 0.4 wt%. Furthermore, dynamic rheological, fluorescence, and cyclic voltammetry measurements are performed to elucidate the gelation mechanism. The results reveal that hydrogen bonds and hydrophobic interactions play a dominant role in gel formation. Subsequently, the properties of the PCAP hydrogels are investigated using rheological measurements, scanning electron microscopy, gravimetric analysis, free radical scavenging, MTT assays, and enzyme-linked immunosorbent assays. The PCAP hydrogels exhibit a porous network structure and cytocompatibility, in addition to good viscoelastic, thixotropic, water-holding, swelling, antioxidant, and anti-inflammatory activities. Furthermore, using rhein as a model drug for encapsulation, it is demonstrated that its cumulative release behaviour from the PCAP hydrogel is pH dependent. These results indicate the potential of PCAP hydrogels for application in biological medicine and drug delivery., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2023. Published by Elsevier B.V.)

The rational structural design of the electrode materials is significant to enhance the electrochemical performance for potassium ion storage, benefiting from the shortened ion diffusion distance, increased conductivity, and pseudo-capacitance promotion. Herein, hydrated vanadium oxide (HVO) nanosheets with enriched oxygen defects are well confined into hollow mesoporous carbon spheres (HMCS), producing O d -VOH@C nanospheres through one-step hydrothermal reaction. Attributed to the restricted growth in the HMCS, the HVO nanosheets are loosely packed, generating abundant interfacial boundaries and large specific areas. As a result, O d -VOH@C nanospheres show increased reaction kinetics and well buffer the volume effects for the K + storage. O d -VOH@C delivers stable capacities of 138 mAh g -1 at 2.0 A g -1 over 10 000 cycles in half-cells attributed to the high pseudo-capacitance contribution. The K + storage mechanism of insertion and conversion reaction is confirmed by ex situ X-ray diffraction, Raman, and X-ray photoelectron spectroscopy analyses. Moreover, the symmetric potassium-ion capacitors of O d -VOH@C//O d -VOH@C deliver a high energy density of 139.6 Wh kg -1 at the power density of 948.3 W kg -1 ., (© 2023 Wiley-VCH GmbH.)

Triple-negative breast cancer (TNBC) is an aggressive subtype of BC characterized by extensive intratumoral heterogeneity. Compared with other types of BC, TNBC is more prone to invasion and metastasis. The aim of the present study was to determine whether adenovirus-mediated clustered regulatory interspaced short palindromic repeats (CRISPR)/Cas9 system is capable of effectively targeting enhancer of zeste homolog 2 (EZH2) in TNBC cells and lay an experimental basis for the investigation of the CRISPR/Cas9 system as a gene therapy for BC. In the present study, EZH2 was knocked out in MDA-MB-231 cells using the CRISPR/Cas9 gene editing tool to create EZH2-knockout (KO) group (EZH2-KO group). Moreover, the GFP knockout group (control group), and a blank group (Blank group), were employed. The success of vector construction and EZH2-KO were verified by T7 endonuclease I (T7EI) restriction enzyme digestion, mRNA detection and western blotting. Changes in proliferation and migration ability of MDA-MB-231 cells following gene editing were detected by MTT, wound healing, Transwell and in vivo tumor biology assays. As indicated by the results of mRNA and protein detection, the mRNA and protein expression of EZH2 were significantly downregulated in the EZH2-KO group. The difference in EZH2 mRNA and protein between the EZH2-KO and the two control groups was statistically significant. MTT, wound healing and transwell assay suggested that the proliferation and migration ability of MDA-MB-231 cells in the EZH2-KO group were significantly decreased after EZH2 knockout. In vivo , the tumor growth rate in the EZH2-KO group was significantly lower than that in the control groups. In brief, the present study revealed that the biological functions of tumor cells were inhibited after EZH2 knockout in MDA-MB-231 cells. The aforementioned findings suggested that EZH2 can have a key role in the development of TNBC., Competing Interests: The authors declare that they have no competing interests., (Copyright: © Mao et al.)

As a means of environmental enrichment, music environment has positive and beneficial effects on biological neural development. Kunming white mice (61 days old) were randomly divided into the control group (group C), the group of D-tone (group D), the group of A-tone (group A) and the group of G-tone (group G). They were given different tonal music stimulation (group A) for 14 consecutive days (2 h/day) to study the effects of tonal music on the neural development of the hippocampus and prefrontal cortex of mice in early life and its molecular mechanisms. The results showed that the number of neurons in the hippocampus and prefrontal cortex of mice increased, with the cell morphology relatively intact. In addition, the number of dendritic spines and the number of dendritic spines per unit length were significantly higher than those in group C, and the expressions of synaptic plasticity proteins (SYP and PSD95) were also significantly elevated over those in group C. Compared with group C, the expression levels of BDNF, TRKB, CREB, PI3K, AKT, GS3Kβ, PLCγ1, PKC, DAG, ERK and MAPK genes and proteins in the hippocampus and prefrontal cortex of mice in the music groups were up-regulated, suggesting that different tones of music could regulate neural development through BDNF and its downstream pathways. The enrichment environment of D-tone music is the most suitable tone for promoting the development of brain nerves in early-life mice. Our study provides a basis for screening the optimal tone of neuroplasticity in early-life mice and for the treatment of neurobiology and neurodegenerative diseases.

Chlorella vulgaris is one of the most commonly used microalgae in aquaculture feeds. It contains high concentrations of various kinds of nutritional elements that are involved in the physiological regulation of aquaculture animals. However, few studies have been conducted to illustrate their influence on the gut microbiota in fish. In this work, the gut microbiota of Nile tilapia ( Oreochromis niloticus ) (average weight is 6.64 g) was analyzed by high-throughput sequencing of the 16S rRNA gene after feeding with 0.5% and 2% C. vulgaris additives in diets for 15 and 30 days (average water temperature was 26 °C). We found that the impact of C. vulgaris on the gut microbiota of Nile tilapia was feeding-time dependent. Only by feeding for 30 days (not 15 days) did the addition of 2% C. vulgaris to diets significantly elevate the alpha diversity (Chao1, Faith pd, Shannon, Simpson, and the number of observed species) of the gut microbiota. Similarly, C. vulgaris exerted a significant effect on the beta diversity (Bray-Curtis similarity) of the gut microbiota after feeding for 30 days (not 15 days). During the 15-day feeding trial, LEfSe analysis showed that Paracoccus , Thiobacillus , Dechloromonas , and Desulfococcus were enriched under 2% C. vulgaris treatment. During the 30-day feeding trial, Afipia , Ochrobactrum , Polymorphum , Albidovulum , Pseudacidovorax , and Thiolamprovum were more abundant in 2% C. vulgaris- treated fish. C. vulgaris promoted the interaction of gut microbiota in juvenile Nile tilapia by increasing the abundance of Reyranella . Moreover, during the feeding time of 15 days, the gut microbes interacted more closely than those during the feeding time of 30 days. This work will be valuable for understanding how C. vulgaris in diets impacts the gut microbiota in fish.

Blindness caused by corneal damage affects millions of people worldwide, and this number continues to rise. However, rapid epithelization and a stable epithelium process are the two biggest challenges for traditional corneal materials. These processes are related to corneal curvature, which is an important factor in determination of the corneal healing process and epithelial behavior during corneal damage. In this study, smooth 3D-printed convex corneal implants based on gelatin methacrylate and collagen are generated. As epithelium distribution and adhesion vary in different regions of the natural cornea, this work separates the surfaces into four regions and studies how cells sense topological cues on curvature. It is found that rabbit corneal epithelial cells (RCECs) seeded on steeper slope gradient surfaces on convex structures result in more aligned cell organization and tighter cell-substrate adhesion, which can also be verified through finite element simulation and signaling pathway analysis. In vivo transplantation of convex implants result in a better fit with adjacent tissue and stronger cell adhesion than flat implants, thereby accelerating corneal epithelialization and promoting collagen fibers and neural regeneration within 180 days. Taken together, printed convex corneal implants that facilitate corneal regeneration may offer a translational strategy for the treatment of corneal damage., (© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Fluorescent substances exist in various aquatic environments and other environmental media. It is a critical task to identify the components accurately and quantify their contents precisely. Based on the Crosstalk Fluorescence Spectroscopy Analysis (CFSA) model, a fluorescence spectroscopic decomposition using the Alternating Gradient Descent (AGD) algorithm is developed. By reducing the residual error of the model through alternating iterations, the CFSA-AGD method achieves unsupervised model training and automatic spectroscopic decomposition without extra experimental operations such as dilution or absorbance measurement, exempting from tedious modeling process. The objectives of this work are to validate that the CFSA-AGD method can comprehensively address the decomposition of fluorescence spectral crosstalk. Furthermore, the novel method is applied to the spectroscopic decomposition of natural FDOMs in aquatic environments as a standard tool. The spectral data analyzing the performance of this method is verified and compared with the conventional methods through the experiment on standard samples. The results indicate that CFSA-AGD has higher spectroscopic decomposition accuracy and gives more abundant information on the characteristic spectra with less residual error than parallel factor analysis. This means that the fluorescence spectra of natural FDOMs can be decomposed into the characteristic fluorescence emission spectra of single components with higher accuracy and the characteristic fluorescence absorption spectra that cannot be obtained by the conventional methods. Meanwhile, it improves the analytical precision of the contents (from R 2 ≥ 0.9778 to R 2 ≥ 0.9920) and reduces the ultimate residual error by two orders of magnitude (from 1.42 × 10 -1 to 4.68 × 10 -3 ) when the method is used to estimate the measured fluorescence spectra., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Fluorescence spectroscopy is a reliable and widely used analytical method. The fluorescence inner filter effect (IFE) is one of the main obstacles in the application of fluorescence spectroscopy and an error source in fluorescence analysis, resulting in the fluorescence spectrum distortion, the spectral shape distortion, and the nonlinearity between fluorescence intensity and fluorophore concentration. An optimized parameter reflecting the self-absorption effect - the fluorescence attenuation absorption index of secondary inner filter effect (sIFE) n opt - is proposed in this paper. Considering the received fluorescence in a direction perpendicular to the incident light, it is related to the solute-solvent system of the fluorescent substance, neither the geometric parameters of the cuvette and the light beam nor the concentration of the fluorescent substance. n opt can accurately reflect the degree to which the fluorescence is affected by the sIFE and correct for any non-ideality of the shapes of excitation/emission beams. The principle and determination method of n opt are explained in detail. Accordingly, an algorithm for the fluorescence spectroscopic correction by n opt is designed. To verify the method, the fluorescence spectra and absorbance spectra of the solutions of fluorescein sodium, rhodamine B, rhodamine 6G, and chlorophyll-a with a series of concentration gradients were measured, respectively. The influence of solvent effect on sIFE correction was also studied. The experiments show that different solute-solvent systems of the fluorescent substances have their own n opt . The novel algorithm can determine the n opt , correct the intensity attenuation and the peak red-shift of the fluorescence spectrum caused by the sIFE, expand the linear range of the concentration predicted by the fluorescence intensity, reduce the error of the prediction model, and improve the measurement accuracy., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Unfolding the "black-box" associations between genotype and phenotype is essential for understanding the molecular mechanisms of complex human diseases. Here, we describe the use of GRPath to uncover putative causal paths (pcPaths) from genetic variants to disease phenotypes. GRPath takes multiple omics data and summary statistics as input and identifies pcPaths that link the putative causal region (pcRegion), putative causal variant (pcVariant), putative causal gene (pcGene), noteworthy cell type, and disease phenotype. For complete details on the use and execution of this protocol, please refer to Xi et al. (2022). 1 ., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

A UV hyperspectral instrument was designed with a polarization measurement channel for real-time in-orbit polarization correction to reduce the influence of polarization on the detection accuracy of atmospheric radiation. One of the prerequisites for in-orbit polarization calibration is accurately calibrating the instrument's polarization properties in the laboratory. This study first introduces the calibration method and measuring device of the polarization characteristics of the ultraviolet (UV) hyperspectral detector and conducts a polarization calibration test of the instrument. The two main error sources introduced by the calibration device were emphatically analyzed, and the correction method of the error sources was deduced theoretically. Finally, the polarization calibration results of the UV hyperspectral detector were corrected, and the uncertainty analysis of the corrected calibration results was about 1.4%, which provides effective ground polarization calibration data for the on-orbit polarization correction of the instrument.

Fluorescence quantitative analysis methods are extensively used in biomedicine inspection, petrochemical industry, environmental monitoring, and many other fields in the past decades. When the analyte is composed of multiple compositions, the accuracy of the conventional method declines significantly due to the fluorescence spectral crosstalk. In this research, the interactions between the light and the multiple compositions are comprehensively analyzed. The concepts of the quenching due to mutual absorption and the fluorescence overlapping are considered, and the mechanism of multi-composition fluorescence emission under single-wavelength excitation light is analyzed theoretically. The mixture experiment and the dilution experiment are designed to illustrate that the quenching due to mutual absorption has a significant nonlinear impact on fluorescence quantitative analysis and the mechanism of fluorescence spectral crosstalk gives a good explanation for these experiments. Through the in-depth theoretical analysis, the computer simulation, and the experiments, a novel principle named the Crosstalk Fluorescence Spectroscopy Analysis (CFSA) is proposed and verified, which has much higher quantitative analysis accuracy (R 2 >0.99 and RMSE≤0.2) than the conventional methods when analyzing the multi-composition samples. Unlike many correction approaches to fluorescence spectroscopy, the novel CFSA can serve as a complete analysis method rather than a correction method. These concepts and the principle are expected to be applied in many practical analysis fields., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Laser-induced breakdown spectroscopy system based on high-repetition-rate microchip laser (HR-LIBS) has been widely used in elemental analysis due to its high energy stability, good portability and fast spectral acquisition speed. However, repeated ablation on powder pellets like soil and coal using HR-LIBS system encounters the problem of serious decline in measurement accuracy. In this work, the relationship between laser ablation and scanning parameters, their correlation with spectral intensity, as well as the optimization approach were fundamentally studied. The correlations among the crater overlapping rate, crater depth and spectral intensity were obtained. An HR-LIBS system with microchip laser (4 kHz repetition rate, 100 µJ laser pulse energy) to perform repeated scanning ablation was established. A theoretical model of the ablation crater morphology for repeated scanning ablation was developed. By taking soil pellets as the experimental samples, the linear fitting curves of crater depth and the spectral intensity ratio were established with the R 2 of 0.90∼0.99. The experimental results showed that as the crater depth developed during repeated ablation, the Si-normalized spectral intensity decreased, and thus the spectral repeatability decreased. It was found that by optimizing the overlapping rate to form a flat crater bottom, the confinement effect of the crater on the plasma could be avoided. As a result, the spectral repeatability was significantly improved. The relative standard deviation (RSD) of Si-normalized spectral intensity was improved from 5% to 0.6%. Finally, repeated ablation was performed with the optimized overlapping rate on soil pellets. The R 2 of calibration curves of Fe, Mg, Ca, and Al were all above 0.993, and the average RSDs were between 0.5% and 1%. This study provides a fast, accurate, and stable method for the analysis of the samples consisting of various materials with high heterogeneity.

Complex traits such as cardiovascular diseases (CVD) are the results of complicated processes jointly affected by genetic and environmental factors. Genome-wide association studies (GWAS) identified genetic variants associated with diseases but usually did not reveal the underlying mechanisms. There could be many intermediate steps at epigenetic, transcriptomic, and cellular scales inside the black box of genotype-phenotype associations. In this article, we present a machine-learning-based cross-scale framework GRPath to decipher putative causal paths (pcPaths) from genetic variants to disease phenotypes by integrating multiple omics data. Applying GRPath on CVD, we identified 646 and 549 pcPaths linking putative causal regions, variants, and gene expressions in specific cell types for two types of heart failure, respectively. The findings suggest new understandings of coronary heart disease. Our work promoted the modeling of tissue- and cell type-specific cross-scale regulation to uncover mechanisms behind disease-associated variants, and provided new findings on the molecular mechanisms of CVD., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

Clarification basins are ubiquitous water treatment units applied across urban water systems. Diverse applications include stormwater systems, stabilization lagoons, equalization, storage and green infrastructure. Residence time (RT), surface overflow rate (SOR) and the Storm Water Management Model (SWMM) are readily implemented but are not formulated to optimize basin geometrics because transport dynamics remain unresolved. As a result, basin design yields high costs from hundreds of thousands to tens of million USD. Basin optimization and retrofits can benefit from more robust and efficient tools. More advanced methods such as computational fluid dynamics (CFD), while demonstrating benefits for resolving transport, can be complex and computationally expensive for routine applications. To provide stakeholders with an efficient and robust tool, this study develops a novel optimization framework for basin geometrics with machine learning (ML). This framework (1) leverages high-performance computing (HPC) and the predictive capability of CFD to provide artificial neural network (ANN) development and (2) integrates a trained ANN model with a hybrid evolutionary-gradient-based optimization algorithm through the ANN automatic differentiation (AD) functionality. ANN model results for particulate matter (PM) clarification demonstrate high predictive capability with a coefficient of determination (R 2 ) of 0.998 on the test dataset. The ANN model for total PM clarification of three (3) heterodisperse particle size distributions (PSDs) also illustrates good performance (R 2 >0.986). The proposed framework was implemented for a basin and watershed loading conditions in Florida (USA), the ML basin designs yield substantially improved cost-effectiveness compared to common designs (square and circular basins) and RT-based design for all PSDs tested. To meet a presumptive regulatory criteria of 80% PM separation (widely adopted in the USA), the ML framework yields 4.7X to 8X lower cost than the common basin designs tested. Compared to the RT-based design, the ML design yields 5.6X to 83.5X cost reduction as a function of the finer, medium, and coarser PSDs. Furthermore, the proposed framework benefits from ANN's high computational efficiency. Optimization of basin geometrics is performed in minutes on a laptop using the framework. The framework is a promising adjuvant tool for cost-effective and sustainable basin implementation across urban water systems., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

eQTL studies are essential for understanding genomic regulation. The effects of genetic variations on gene regulation are cell-type-specific and cellular-context-related, so studying eQTLs at a single-cell level is crucial. The ideal solution is to use both mutation and expression data from the same cells. However, the current technology of such paired data in single cells is still immature. We present a new method, eQTLsingle, to discover eQTLs only with single-cell RNA-seq (scRNA-seq) data, without genomic data. It detects mutations from scRNA-seq data and models gene expression of different genotypes with the zero-inflated negative binomial (ZINB) model to find associations between genotypes and phenotypes at the single-cell level. On a glioblastoma and gliomasphere scRNA-seq dataset, eQTLsingle discovered hundreds of cell-type-specific tumor-related eQTLs, most of which cannot be found in bulk eQTL studies. Detailed analyses on examples of the discovered eQTLs revealed important underlying regulatory mechanisms. eQTLsingle is a uniquely powerful tool for utilizing the vast scRNA-seq resources for single-cell eQTL studies, and it is available for free academic use at https://github.com/horsedayday/eQTLsingle., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Surface overflow rate (SOR), plug flow reactor (PFR) and continuously stirred tank reactor (CSTR) are common models for clarification unit operations (UO). With wide deployment in engineering practice and regulation, through tools from spreadsheets to complex numerical codes, these models are formulated based upon conceptualized system geometry (e.g., rectangular channel) and idealized hydrodynamics (plug flow or well-mixed conditions). Yet the hydrodynamics and geometry of actual UO systems can be complex and substantially different from these assumptions. As a result, the applicability and generalizability of these models require critical and systematic interrogation. This study examines the predictive capability and generalizability of these common models for a hydrodynamic separator (HS), tanks, rectangular clarifiers and an urban drainage basin based on physical model data and high-fidelity large-eddy simulation (LES). Moreover, this study presents a novel application of dynamic similitude to developing a more generalized and physically interpretable model based on the hypothesis that PM and PM-partitioned constituent separation in a UO can be approximated solely through the dimensionless settling velocity W (Hazen number). Based on this hypothesis and dynamic similitude, a similarity modified gamma model (SMG) is proposed and tested. With dynamic similitude and W, results show common models are not robust and generalizable for predicting PM separation with error ranging from 30 to 50% and can significantly oversize a clarifier up to 904%. The non-linear characteristics of PM separation are shown to have a critical role in clarifications system design scalability and economics. In contrast, the SMG model is robust and generalizes the PM separation for geometrically similar systems, irrespective of particle density, particle size distribution (PSD), and loading conditions. The developed theory and proposed SMG model also can simplify and reduce the effort as well as expense of physical model testing while serving as an adjuvant for numerical simulations of clarification systems. Results also reveal commercial HS systems do not outperform simple plain tank geometries. The complex turbulence vortical structures pose significant challenges for UO system analysis and design., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The accumulation of massive single-cell omics data provides growing resources for building biomolecular atlases of all cells of human organs or the whole body. The true assembly of a cell atlas should be cell-centric rather than file-centric. We developed a unified informatics framework for seamless cell-centric data assembly and built the human Ensemble Cell Atlas (hECA) from scattered data. hECA v1.0 assembled 1,093,299 labeled human cells from 116 published datasets, covering 38 organs and 11 systems. We invented three new methods of atlas applications based on the cell-centric assembly: " in data " cell sorting for targeted data retrieval with customizable logic expressions, "quantitative portraiture" for multi-view representations of biological entities, and customizable reference creation for generating references for automatic annotations. Case studies on agile construction of user-defined sub-atlases and " in data " investigation of CAR-T off-targets in multiple organs showed the great potential enabled by the cell-centric ensemble atlas., Competing Interests: The authors declare no competing interests. The database technology behind the uGT data storage used in hECA is being applied for a patent., (© 2022 The Author(s).)

Background: Drug resistance is a critical obstacle in cancer therapy. Discovering cancer drug response is important to improve anti-cancer drug treatment and guide anti-cancer drug design. Abundant genomic and drug response resources of cancer cell lines provide unprecedented opportunities for such study. However, cancer cell lines cannot fully reflect heterogeneous tumor microenvironments. Transferring knowledge studied from in vitro cell lines to single-cell and clinical data will be a promising direction to better understand drug resistance. Most current studies include single nucleotide variants (SNV) as features and focus on improving predictive ability of cancer drug response on cell lines. However, obtaining accurate SNVs from clinical tumor samples and single-cell data is not reliable. This makes it difficult to generalize such SNV-based models to clinical tumor data or single-cell level studies in the future., Results: We present a new method, DualGCN, a unified Dual Graph Convolutional Network model to predict cancer drug response. DualGCN encodes both chemical structures of drugs and omics data of biological samples using graph convolutional networks. Then the two embeddings are fed into a multilayer perceptron to predict drug response. DualGCN incorporates prior knowledge on cancer-related genes and protein-protein interactions, and outperforms most state-of-the-art methods while avoiding using large-scale SNV data., Conclusions: The proposed method outperforms most state-of-the-art methods in predicting cancer drug response without the use of large-scale SNV data. These favorable results indicate its potential to be extended to clinical and single-cell tumor samples and advancements in precision medicine., (© 2022. The Author(s).)

Particulate matter (PM), while not an emerging contaminant, remains the primary labile substrate for partitioning and transport of emerging and known chemicals and pathogens. As a common unit operation and also green infrastructure, clarification basins are widely implemented to sequester PM as well as PM-partitioned chemicals and pathogens. Despite ubiquitous application for urban drainage, stormwater clarification basin design and optimization lacks robust and efficient design guidance and tools. Current basin design and regulation primarily adopt residence time (RT) as presumptive guidance. This study examines the accuracy and generalizability of RT and nondimensional groups of basin geometric and dynamic similarity (Hazen, Reynolds, Schmidt numbers) to scale clarification basin performance (measured as PM separation and total PM separation). Published data and 160,000 computational fluid dynamics (CFD) simulations of basin PM separation over a wide range of basin configurations, loading conditions, and PM granulometry (particle size distribution [PSD], density) are examined. Based on the CFD database, a novel implementation of machine learning (ML) models: decision tree (DT), random forest (RF), artificial neural networks (ANN), and symbolic regression (SR) are developed and trained as surrogate models for basin PM separation predictions. Study results indicate that: (1) Models based solely on RT are not accurate or generalizable for basin PM separation, with significant differences between CFD and RT models primarily for RT < 200 hr, (2) RT models are agnostic to basin configurations and PM granulometrics and therefore do not reproduce total PM separation, (3) Trained ML models provide high predictive capability, with (R 2 ) above 0.99 and prediction for total PM separation within ±15%. In particular, the SR model distilled from CFD simulations is entirely defined by only two compact algebraic equations (allowing use in a spreadsheet tool). The SR model has a physical basis and indicates PM separation is primarily a function of the Hazen number and basin horizontal and vertical aspect ratios, (4) With common presumptive guidance of 80% for PM separation, a Pareto frontier analysis indicates that the CFD-ML augmented SR model generates significant economic benefit for basin planning/design, and (5) CFD-ML models show that enlarging basin dimensions (increasing RT) to address impaired behavior can result in exponential cost increases, irrespective of land/infrastructure adjacency conflicts. CFD-ML applications can extend to intra-basin retrofits (permeable baffles) to upgrade impaired basins., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Due to the rapid growth of web conferences, remote screen sharing, and online games, screen content has become an important type of internet media information and over 90% of online media interactions are screen based. Meanwhile, as the main component in the screen content, textual information averagely takes up over 40% of the whole image on various commonly used screen content datasets. However, it is difficult to compress the textual information by using the traditional coding schemes as HEVC, which assumes strong spatial and temporal correlations within the image/video. State-of-the-art screen content coding (SCC) standard as HEVC-SCC still adopts a block-based coding framework and does not consider the text semantics for compression, thus inevitably blurring texts at a lower bitrate. In this paper, we propose a general text semantic-aware screen content coding scheme (TSA-SCC) for ultra low bitrate setting. This method detects the abrupt picture in a screen content video (or image), recognizes textual information (including word, position, font type, font size and font color) in the abrupt picture based on neural networks, and encodes texts with text coding tools. The other pictures as well as the background image after removing texts from the abrupt picture via inpainting, are encoded with HEVC-SCC. Compared with HEVC-SCC, the proposed method TSA-SCC reduces bitrate by up to 3× at a similar compression quality. Moreover, TSA-SCC achieves much better visual quality with less bitrate consumption when encoding the screen content video/image at ultra low bitrates.

Cu chips are cheaper than Ag and Au chips for practical SERS applications. However, copper substrates generally have weak SERS enhancement effects and poor stability. In the present work, Cu-based SERS chips with high sensitivity and stability were developed by a chemical reduction method. In the preparation process, Cu NPs were densely deposited onto fabric supports. The as-prepared Cu-coated fabric was hydrophobic with fairly good SERS performance. The Cu-coated fabric was able to be used as a SERS chip to detect crystal violet, and it exhibited an enhancement factor of 2.0 × 106 and gave a limit of detection (LOD) as low as 10-8 M. The hydrophobicity of the Cu membrane on the fabric is favorable to cleaning background interference signals and promoting the stability of Cu NPs to environment oxidation. However, this Cu SERS chip was still poor in its long-term stability. The SERS intensity on the chip was decreased to 18% of the original one after it was stored in air for 60 days. A simple introduction of Ag onto the clean Cu surface was achieved by a replacement reaction to further enhance the SERS performances of the Cu chips. The Ag-modified Cu chips showed an increase of the enhancement factor to 7.6 × 106 due to the plasmonic coupling between Cu and Ag in nanoscale, and decreased the LOD of CV to 10-11 M by three orders of magnitude. Owing to the additional protection of Ag shell, the SERS intensity of the Cu-Ag chip after a two-month storing maintained 80% of the original intensity. The Cu-Ag SERS chips were also applied to detect other organics, and showing wide linearity range and low LOD values for the quantitative detection.

Electro-Fenton (EF) process is a significant water treatment method for the degradation of dissolved organic matter (DOM) in water and has been widely studied and applied in the past decade to degrade various dissolved organics. During water treatment, in order to monitor the degradation efficiency, it is in dire need to develop rapid and accurate methods that are favorable to assay on-line and on-site to provide feedback in a timely manner. UV-Visible absorption spectroscopy and Excitation-Emission Matrix (EEM) fluorescence spectroscopy techniques are most potential to realize on-line DOM measurement, but the measurement accuracy is unsatisfactory because of the obligatory involvement of iron-containing interferents. This study aims to simplify the measurement system complexity while overcoming the effect of iron-containing interferents during the measurement. An intrinsic relationship between the measured DOM concentration and the ultraviolet absorption at λ 1 and the light intensities of the fluorescence emission at λ 2 is derived theoretically and proved, based on which the influence of iron ions and their complexes on the spectrum can be eliminated, thus the content of DOM in the Electro-Fenton process is accurately determined. The proposed dual wavelength analysis with combination of fluorescence emission and ultraviolet absorption spectroscopy can achieve high precision (R 2 =0.9882,RMSE=0.0131mg/L). Furthermore, the on-line measurement design, called ultraviolet absorption-fluorescence emission dual wavelength analyzer, only includes one ultraviolet LED and two photodetectors. Its structure is simple and suitable for on-line monitoring DOM in EF process., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Increasing evidence demonstrate that the gut microbiota is involved in the pathogenesis of liver diseases, and faecal microbiota transplantation is considered to be a promising new treatment option. However, there are no reports on the intestinal flora of asymptomatic HBV carriers using next-generation sequencing. This study intends to investigate the potential role of the intestinal microflora in predicting the progression of Hepatitis B patients in different non-cancerous stages., Results: A total of 266 patients with different stages of Hepatitis B and 31 healthy controls were included in this study. Some of the subjects (217 cases) underwent 16S rRNA gene sequencing. Compared with the control group (CK), the α diversity of patients in Group A (HBV carrier) slightly increased, while that of patients in the other three groups decreased. Each group of patients, especially those in Group C (cirrhosis) and Group D (acute-on-chronic liver failure), could be separated from the CK using weighted UniFrac PCoA and ANOSIM. LEfSe revealed that 40 taxa belonging to three phyla had an LDA larger than 4. In addition to the comparison between Group B (chronic Hepatitis B) and Group C, the specific flora and potential taxonomic function were also identified. Different microbial communities were found to be highly correlated with clinical indicators and the Child-Pugh scores. Changes in the microbial community were highly related to the alternations of host metabolism, which in turn, was related to the development of Hepatitis B. Our analysis identified a total of 47 strains with potential biomarker functions at all levels except for the phylum level., Conclusions: Faecal microbiota transplantation of some potential beneficial bacteria can change with the occurrence of disease, and HBV carriers might be the most suitable donors., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s) 2020.)

Clarifiers integrating radial cartridge filtration (RCF) are a combined unit operation variant of millennia-old sedimentation-filtration systems. Similarly, RCF is a primarily horizontal flow variant with flow orthogonal to gravity and a radial velocity gradient, in contrast to traditional deep-bed vertical filtration. These granular filters function at lower finite granular Reynolds numbers. A proposed computational fluid dynamics framework, implementing the Navier-Stokes equations, couples a pore-scale filter model with a macroscopic scale sedimentation-filtration model to create a tool examining non-Brownian particle separation. Validation is conducted using previous physical testing from a full-scale sedimentation-filtration system under steady flow and particulate loads. Model results illustrate a two-zone filtration structure with respect to particle diameter, similar to vertical filtration. The computational tool predicts particulate matter separation of 86.1% compared to 87.8% for physical testing. The physical-based computational framework does not need high-level calibration as compared to analytical, lumped, or empirical models; conferring direct extensibility to similar unit operation systems. The novel multi-scale tool simulates particulate matter fate in a modern re-incarnation of a sedimentation-filtration unit operation. The tool functions as an adjuvant that complements regulatory or certification testing. The tool can provide guidance for design or maintenance as well as system management with respect to particle fate in, and breakthrough from, granular filters in a combined unit operation., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Microwave-ultrasonic assisted aqueous enzymatic extraction (MUAAEE) was applied to extract tiger nut oil (TNO). The conditions of MUAAEE were optimized by Plackett-Burman design followed Box-Behnken design. An oil recovery of 85.23% was achieved under optimum conditions of a 2% concentration of mixed enzyme including cellulase, pectinase and hemicellulase (1/1/1, w/w/w), particle size <600 μm, microwave power 300 W, ultrasonic power 460 W, radiation temperature 40 °C, time 30 min, enzymolysis temperature 45 °C, pH 4.9, liquid-to-solid ratio 10 mL/g and time 180 min. Oil by MUAAEE revealed the similar fatty acid compositions, triglyceride compositions, thermal behaviour and flavour compared with oil by Soxhlet extraction (SE), while the oil quality of MUAAEE is superior to that of SE. Scanning electron microscopy revealed that structural disruption of tiger nut caused by MUAAEE facilitated the oil extraction. Results suggest that MUAAEE could be an efficient and environment-friendly method for extraction of TNO., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier B.V.)

By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results., Competing Interests: The authors declare no conflicts of interest.

There are many challenges in the determination of elements in complex matrix such as soil, coal and minerals by laser induced breakdown spectroscopy (LIBS) method. Due to the influence of matrix effect, instability of laser plasma and fluctuation of laser parameters, the repeatability and accuracy of quantitative results are always not satisfactory. In order to improve the accuracy, high-energy laser (30mJ-100mJ) with precise control was utilized in many laboratories. In this paper, quantitative analysis of copper in copper concentrate by low-energy (10µJ) LIBS is studied. In order to reduce the influence of matrix effect and other factors, a partial least square regression method based on double genetic algorithm (DGA-PLS) is proposed. The detail operations are as follow: the reference spectral lines are automatically selected by GA as the optimal internal standard for spectral normalization. Then the GA is used to select variables from the normalized spectra for PLS. The results showed that, for univariate model, the coefficient of determination (R 2 ) was improved from 0.6 to 0.97 by the optimal internal standard normalization. Compared with tradition PLS, the root mean square error of cross validation (RMSECV) and root mean square error of prediction (RMSEP) of PLS trained by the normalized spectral data decreased from 1.4% and 0.42% to 0.9% and 0.29% respectively. Compared with the normalized PLS, the RMSECV and RMSEP of the DGA-PLS trained by the normalized and feature selected spectral data decreased from 0.9% and 0.29% to 0.26% and 0.21% respectively. The results show that DGA-PLS can significantly reduce matrix effect, improve prediction accuracy and reduce the risk of overfitting in determination of copper in copper concentrate.

Conformation capture technologies (e.g., Hi-C) chart physical interactions between chromatin regions on a genome-wide scale. However, the structural variability of the genome between cells poses a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range and interchromosomal interactions. Here, we present a probabilistic approach for deconvoluting Hi-C data into a model population of distinct diploid 3D genome structures, which facilitates the detection of chromatin interactions likely to co-occur in individual cells. Our approach incorporates the stochastic nature of chromosome conformations and allows a detailed analysis of alternative chromatin structure states. For example, we predict and experimentally confirm the presence of large centromere clusters with distinct chromosome compositions varying between individual cells. The stability of these clusters varies greatly with their chromosome identities. We show that these chromosome-specific clusters can play a key role in the overall chromosome positioning in the nucleus and stabilizing specific chromatin interactions. By explicitly considering genome structural variability, our population-based method provides an important tool for revealing novel insights into the key factors shaping the spatial genome organization.

Type 2 corticotropin-releasing factor receptor (CRFR2) is expressed in skeletal muscle and stimulation of the receptor has been shown to inhibit the effect of insulin on glucose uptake in muscle cells. Currently, little is known about the mechanisms underlying this process. In this study, we first showed that both in vivo and in vitro CRFR2 expression in muscle was closely correlated with insulin sensitivity, with elevated receptor levels observed in insulin resistant muscle cells. Stimulation of CRFR2 by urocortin 2 (Ucn 2), a CRFR2-selective ligand, in C2C12 myotubes greatly attenuated insulin-induced glucose uptake. The inhibitory effect of CRFR2 signaling required cAMP production and is involved the mammalian target of rapamycine pathway, as rapamycin reversed the inhibitory effect of CRFR2 stimulation on insulin-induced glucose uptake. Moreover, stimulation of CRFR2 failed to inhibit glucose uptake in muscle cells induced by platelet-derived growth factor, which, similar to insulin, signals through Akt-mediated pathway but is independently of insulin receptor substrate (IRS) proteins to promote glucose uptake. This result argues that CRFR2 signaling modulates insulin's action likely at the levels of IRS. Consistent with this notion, Ucn 2 reduced insulin-induced tyrosine phosphorylation of IRS-1, and treatment with rapamycin reversed the inhibitory effect of Ucn 2 on IRS-1 and Akt phosphorylation. In conclusion, the inhibitory effect of CRFR2 signaling on insulin action is mediated by cAMP in a mammalian target of rapamycine-dependent manner, and IRS-1 is a key nodal point where CRFR2 signaling modulates insulin-stimulated glucose uptake in muscle cells.

We examine the problem of finite Fermi systems having a degenerate single-particle spectrum and show that the Landau approach, applied to such a system, admits the possibility of merging single-particle levels. It is demonstrated that the opportunity for this behavior is widespread in quantum many-body systems. The salient feature of the phenomenon is the occurrence of nonintegral quasiparticle occupation numbers, leading to a radical alteration of the standard quasiparticle picture. Implications of this alteration are considered for nuclear, atomic, and solid-state systems.