CodLncScape Provides a Self-Enriching Framework for the Systematic Collection and Exploration of Coding LncRNAs.

Recent studies have revealed that numerous lncRNAs can translate proteins under specific conditions, performing diverse biological functions, thus termed coding lncRNAs. Their comprehensive landscape, however, remains elusive due to this field's preliminary and dispersed nature. This study introduces codLncScape, a framework for coding lncRNA exploration consisting of codLncDB, codLncFlow, codLncWeb, and codLncNLP. Specifically, it contains a manually compiled knowledge base, codLncDB, encompassing 353 coding lncRNA entries validated by experiments. Building upon codLncDB, codLncFlow investigates the expression characteristics of these lncRNAs and their diagnostic potential in the pan-cancer context, alongside their association with spermatogenesis. Furthermore, codLncWeb emerges as a platform for storing, browsing, and accessing knowledge concerning coding lncRNAs within various programming environments. Finally, codLncNLP serves as a knowledge-mining tool to enhance the timely content inclusion and updates within codLncDB. In summary, this study offers a well-functioning, content-rich ecosystem for coding lncRNA research, aiming to accelerate systematic studies in this field.

SQANTI3: curation of long-read transcriptomes for accurate identification of known and novel isoforms.

SQANTI3 is a tool designed for the quality control, curation and annotation of long-read transcript models obtained with third-generation sequencing technologies. Leveraging its annotation framework, SQANTI3 calculates quality descriptors of transcript models, junctions and transcript ends. With this information, potential artifacts can be identified and replaced with reliable sequences. Furthermore, the integrated functional annotation feature enables subsequent functional iso-transcriptomics analyses.

Cnidii Fructus: A traditional Chinese medicine herb and source of antiosteoporotic drugs.

BACKGROUND: Osteoporosis (OP) is a prevalent chronic metabolic bone disease for which limited countermeasures are available. Cnidii Fructus (CF), primarily derived from Cnidium monnieri (L.) Cusson., has been tested in clinical trials of traditional Chinese medicine for the management of OP. Accumulating preclinical studies indicate that CF may be used against OP. MATERIALS AND METHODS: Comprehensive documentation and analysis were conducted to retrieve CF studies related to its main phytochemical components as well as its pharmacokinetics, safety and pharmacological properties. We also retrieved information on the mode of action of CF and, in particular, preclinical and clinical studies related to bone remodeling. This search was performed from the inception of databases up to the end of 2022 and included PubMed, China National Knowledge Infrastructure, the National Science and Technology Library, the China Science and Technology Journal Database, Weipu, Wanfang, the Web of Science and the China National Patent Database. RESULTS: CF contains a wide range of natural active compounds, including osthole, bergapten, imperatorin and xanthotoxin, which may underlie its beneficial effects on improving bone metabolism and quality. CF action appears to be mediated via multiple processes, including the osteoprotegerin (OPG)/receptor activator of nuclear factor-κB ligand (RANKL)/receptor activator of nuclear factor-κB (RANK), Wnt/ß-catenin and bone morphogenetic protein (BMP)/Smad signaling pathways. CONCLUSION: CF and its ingredients may provide novel compounds for developing anti-OP drugs.

Cm-siRPred: Predicting chemically modified siRNA efficiency based on multi-view learning strategy.

The rational modification of siRNA molecules is crucial for ensuring their drug-like properties. Machine learning-based prediction of chemically modified siRNA (cm-siRNA) efficiency can significantly optimize the design process of siRNA chemical modifications, saving time and cost in siRNA drug development. However, existing in-silico methods suffer from limitations such as small datasets, inadequate data representation capabilities, and lack of interpretability. Therefore, in this study, we developed the Cm-siRPred algorithm based on a multi-view learning strategy. The algorithm employs a multi-view strategy to represent the double-strand sequences, chemical modifications, and physicochemical properties of cm-siRNA. It incorporates a cross-attention model to globally correlate different representation vectors and a two-layer CNN module to learn local correlation features. The algorithm demonstrates exceptional performance in cross-validation experiments, independent dataset, and case studies on approved siRNA drugs, and showcasing its robustness and generalization ability. In addition, we developed a user-friendly webserver that enables efficient prediction of cm-siRNA efficiency and assists in the design of siRNA drug chemical modifications. In summary, Cm-siRPred is a practical tool that offers valuable technical support for siRNA chemical modification and drug efficiency research, while effectively assisting in the development of novel small nucleic acid drugs. Cm-siRPred is freely available at https://cellknowledge.com.cn/sirnapredictor/.

AdipoRon ameliorates the progression of heart failure with preserved ejection fraction via mitigating lipid accumulation and fibrosis.

INTRODUCTION: Obesity and imbalance in lipid homeostasis contribute greatly to heart failure with preserved ejection fraction (HFpEF), the dominant form of heart failure. Few effective therapies exist to control metabolic alterations and lipid homeostasis. OBJECTIVES: We aimed to investigate the cardioprotective roles of AdipoRon, the adiponectin receptor agonist, in regulating lipid accumulation in the two-hit HFpEF model. METHODS: HFpEF mouse model was induced using 60 % high-fat diet plus L-NAME drinking water. Then, AdipoRon (50 mg/kg) or vehicle were administered by gavage to the two-hit HFpEF mouse model once daily for 4 weeks. Cardiac function was evaluated using echocardiography, and Postmortem analysis included RNA-sequencing, untargeted metabolomics, transmission electron microscopy and molecular biology methods. RESULTS: Our study presents the pioneering evidence that AdipoR was downregulated and impaired fatty acid oxidation in the myocardia of HFpEF mice, which was associated with lipid metabolism as indicated by untargeted metabolomics. AdipoRon, orally active synthetic adiponectin receptor agonist, could upregulate AdipoR1/2 (independently of adiponectin) and reduce lipid droplet accumulation, and alleviate fibrosis to restore HFpEF phenotypes. Finally, AdipoRon primarily exerted its effects through restoring the balance of myocardial fatty acid intake, transport, and oxidation via the downstream AMPKα or PPARα signaling pathways. The protective effects of AdipoRon in HFpEF mice were reversed by compound C and GW6471, inhibitors of AMPKα and PPARα, respectively. CONCLUSIONS: AdipoRon ameliorated the HFpEF phenotype by promoting myocardial fatty acid oxidation, decreasing fatty acid transport, and inhibiting fibrosis via the upregulation of AdipoR and the activation of AdipoR1/AMPKα and AdipoR2/PPARα-related downstream pathways. These findings underscore the therapeutic potential of AdipoRon in HFpEF. Importantly, all these parameters get restored in the context of continued mechanical and metabolic stressors associated with HFpEF.

Photonic spin Hall effect driven broadband multi-focus dielectric metalens.

The multi-focus metalens can couple the light into multiple channels in optical interconnections, which is beneficial to the development of planar, miniaturized, and integrated components. We propose broadband photonic spin Hall effect (PSHE) driven multi-focus metalenses, in which each nanobrick plays a positive role for all focal points. Three PSHE driven metalenses with four, six, and eight focal points have been designed and investigated, respectively. Under the incidences of left-/right-handed circularly polarized (LCP/RCP) light, these metalenses can generate regularly distributed two, three, and four RCP/LCP focal points, respectively. The uniformity of the focusing intensity has been investigated in detail by designing an additional four six-focus metalenses with different focus distributions. The uniqueness of these metalenses makes this design philosophy very attractive for applications in spin photonics, compact polarization detection, multi-imaging systems, and information processing systems.

Genomic alterations driving precancerous to cancerous lesions in esophageal cancer development.

Esophageal squamous cell carcinoma (ESCC) develops through a series of increasingly abnormal precancerous lesions. Previous studies have revealed the striking differences between normal esophageal epithelium and ESCC in copy number alterations (CNAs) and mutations in genes driving clonal expansion. However, due to limited data on early precancerous lesions, the timing of these transitions and which among them are prerequisites for malignant transformation remained unclear. Here, we analyze 1,275 micro-biopsies from normal esophagus, early and late precancerous lesions, and esophageal cancers to decipher the genomic alterations at each stage. We show that the frequency of TP53 biallelic inactivation increases dramatically in early precancerous lesion stage while CNAs and APOBEC mutagenesis substantially increase at late stages. TP53 biallelic loss is the prerequisite for the development of CNAs of genes in cell cycle, DNA repair, and apoptosis pathways, suggesting it might be one of the earliest steps initiating malignant transformation.

SQANTI-SIM: a simulator of controlled transcript novelty for lrRNA-seq benchmark.

Long-read RNA sequencing has emerged as a powerful tool for transcript discovery, even in well-annotated organisms. However, assessing the accuracy of different methods in identifying annotated and novel transcripts remains a challenge. Here, we present SQANTI-SIM, a versatile tool that wraps around popular long-read simulators to allow precise management of transcript novelty based on the structural categories defined by SQANTI3. By selectively excluding specific transcripts from the reference dataset, SQANTI-SIM effectively emulates scenarios involving unannotated transcripts. Furthermore, the tool provides customizable features and supports the simulation of additional types of data, representing the first multi-omics simulation tool for the lrRNA-seq field.

Aberrant epithelial cell interaction promotes esophageal squamous-cell carcinoma development and progression.

Epithelial-mesenchymal transition (EMT) and proliferation play important roles in epithelial cancer formation and progression, but what molecules and how they trigger EMT is largely unknown. Here we performed spatial transcriptomic and functional analyses on samples of multistage esophageal squamous-cell carcinoma (ESCC) from mice and humans to decipher these critical issues. By investigating spatiotemporal gene expression patterns and cell-cell interactions, we demonstrated that the aberrant epithelial cell interaction via EFNB1-EPHB4 triggers EMT and cell cycle mediated by downstream SRC/ERK/AKT signaling. The aberrant epithelial cell interaction occurs within the basal layer at early precancerous lesions, which expands to the whole epithelial layer and strengthens along the cancer development and progression. Functional analysis revealed that the aberrant EFNB1-EPHB4 interaction is caused by overexpressed ΔNP63 due to TP53 mutation, the culprit in human ESCC tumorigenesis. Our results shed new light on the role of TP53-TP63/ΔNP63-EFNB1-EPHB4 axis in EMT and cell proliferation in epithelial cancer formation.

Conflict with children, psychological depression, and problematic internet use among Chinese older adults: The moderating effect of sociability and living situation.

Introduction: Problematic internet use among the elderly is an emerging area as previous studies focused more among the young people. Only a few studies focused on problematic internet use at the level of individual characteristics of older adults or on mitigating factors at the level of the older adult's family, ignoring family-level disruptive factors. Objective: The purpose of study is to investigate the relationship between conflict with children and problematic internet use among the elderly, as well as the mediating mechanisms and boundary conditions of the relationship. Methods: The valid sample of study composed of 428 older adults from 39 different villages and communities in central China. Data analyses were conducted by SPSS, MPLUS, and SmartPLS software. To test our hypotheses, we implement several quantitative methods, including confirmatory factor analysis (CFA), correlations analysis, and ordinary least squares (OLS) regression. Also, we employed partial least square structural equation modeling (PLS-SEM) for robustness testing. Results: The results indicated that conflict with children was positively associated with problematic internet use of old people; psychological depression mediated the relationship between conflict with children and old adults' problematic internet use; sociability moderated the effect of conflict with children on psychological depression; and living situation moderated the effect of psychological depression on problematic internet use among the elderly. Conclusion: The current research improved the understanding of the mechanisms that produce problematic internet use among the elderly and helped prevent or reduce problematic internet use in older adults in terms of family support systems and individual ability characteristics.

Brain Functional Representation of Highly Occluded Object Recognition.

Recognizing highly occluded objects is believed to arise from the interaction between the brain's vision and cognition-controlling areas, although supporting neuroimaging data are currently limited. To explore the neural mechanism during this activity, we conducted an occlusion object recognition experiment using functional magnetic resonance imaging (fMRI). During magnet resonance examinations, 66 subjects engaged in object recognition tasks with three different occlusion degrees. Generalized linear model (GLM) analysis showed that the activation degree of the occipital lobe (inferior occipital gyrus, middle occipital gyrus, and occipital fusiform gyrus) and dorsal anterior cingulate cortex (dACC) was related to the occlusion degree of the objects. Multivariate pattern analysis (MVPA) further unearthed a considerable surge in classification precision when dACC activation was incorporated as a feature. This suggested the combined role of dACC and the occipital lobe in occluded object recognition tasks. Moreover, psychophysiological interaction (PPI) analysis disclosed that functional connectivity (FC) between the dACC and the occipital lobe was enhanced with increased occlusion, highlighting the necessity of FC between these two brain regions in effectively identifying exceedingly occluded objects. In conclusion, these findings contribute to understanding the neural mechanisms of highly occluded object recognition, augmenting our appreciation of how the brain manages incomplete visual data.

Real-Time Classification of Motor Imagery Using Dynamic Window-Level Granger Causality Analysis of fMRI Data.

This article presents a method for extracting neural signal features to identify the imagination of left- and right-hand grasping movements. A functional magnetic resonance imaging (fMRI) experiment is employed to identify four brain regions with significant activations during motor imagery (MI) and the effective connections between these regions of interest (ROIs) were calculated using Dynamic Window-level Granger Causality (DWGC). Then, a real-time fMRI (rt-fMRI) classification system for left- and right-hand MI is developed using the Open-NFT platform. We conducted data acquisition and processing on three subjects, and all of whom were recruited from a local college. As a result, the maximum accuracy of using Support Vector Machine (SVM) classifier on real-time three-class classification (rest, left hand, and right hand) with effective connections is 69.3%. And it is 3% higher than that of traditional multivoxel pattern classification analysis on average. Moreover, it significantly improves classification accuracy during the initial stage of MI tasks while reducing the latency effects in real-time decoding. The study suggests that the effective connections obtained through the DWGC method serve as valuable features for real-time decoding of MI using fMRI. Moreover, they exhibit higher sensitivity to changes in brain states. This research offers theoretical support and technical guidance for extracting neural signal features in the context of fMRI-based studies.

Collagen 1-mediated CXCL1 secretion in tumor cells activates fibroblasts to promote radioresistance of esophageal cancer.

Esophageal squamous-cell carcinoma (ESCC) is commonly treated with radiotherapy; however, radioresistance hinders its clinical effectiveness, and the underlying mechanism remains elusive. Here, we develop patient-derived xenografts (PDXs) from 19 patients with ESCC to investigate the mechanisms driving radioresistance. Using RNA sequencing, cytokine arrays, and single-cell RNA sequencing, we reveal an enrichment of cancer-associated fibroblast (CAF)-derived collagen type 1 (Col1) and tumor-cell-derived CXCL1 in non-responsive PDXs. Col1 not only promotes radioresistance by augmenting DNA repair capacity but also induces CXCL1 secretion in tumor cells. Additionally, CXCL1 further activates CAFs via the CXCR2-STAT3 pathway, establishing a positive feedback loop. Directly interfering with tumor-cell-derived CXCL1 or inhibiting the CXCL1-CXCR2 pathway effectively restores the radiosensitivity of radioresistant xenografts in vivo. Collectively, our study provides a comprehensive understanding of the molecular mechanisms underlying radioresistance and identifies potential targets to improve the efficacy of radiotherapy for ESCC.

Enhancing perovskite solar cells efficiency through cesium fluoride mediated surface lead iodide modulation.

The presence of an excessive amount of lead iodide on the surface of perovskite solar cells (PSCs) is a significant contributing factor that adversely affects the stability of these devices when exposed to continuous light. To address this issue, we developed an effective strategy involving polishing PbI2 on a perovskite surface using CsF. In this study, we investigated the effects of CsF post-treatment on perovskite films and their photovoltaic properties. The results of the time-resolved photoluminescence and ultraviolet photoelectron spectroscopy tests reveal the significant positive impact of our passivation method based on CsF, which reduces the valence band offset between the perovskite and hole transport layers while simultaneously enhancing the carrier interface transport. PSCs treated with CsF exhibited a photoelectric conversion efficiency (PCE) of 24.25% and an increased fill factor (FF) of 81.72%, which surpassed those of the original PSCs (PCE = 22.12% and FF = 77.40%). Furthermore, after aging for over 2500 h at room temperature and in 30 ± 10% humidity, the PCE of the unpacked PSCs reduced to only 42% of the initial value. Furthermore, the devices treated with CsF maintained their impressive performance, with the PCE maintaining optimal levels at 91% of the initial efficiency.

SQANTI-SIM: a simulator of controlled transcript novelty for lrRNA-seq benchmark.

Long-read RNA-seq has emerged as a powerful tool for transcript discovery, even in well-annotated organisms. However, assessing the accuracy of different methods in identifying annotated and novel transcripts remains a challenge. Here, we present SQANTI-SIM, a versatile utility that wraps around popular long-read simulators to allow precise management of transcript novelty based on the structural categories defined by SQANTI3. By selectively excluding specific transcripts from the reference dataset, SQANTI-SIM effectively emulates scenarios involving unannotated transcripts. Furthermore, the tool provides customizable features and supports the simulation of additional types of data, representing the first multi-omics simulation tool for the lrRNA-seq field. We demonstrate the effectiveness of SQANTI-SIM by benchmarking five transcriptome reconstruction pipelines using the simulated data.

Body color plasticity of Diaphorina citri reflects a response to environmental stress.

Body color polyphenism is common in Diaphorina citri. Previous studies compared physiological characteristics in D. citri, but the ecological and biological significance of its body color polyphenism remains poorly understood. We studied the ecological and molecular effects of stressors related to body color in D. citri. Crowding or low temperature induced a high proportion of gray morphs, which had smaller bodies, lower body weight, and greater susceptibility to the insecticide dinotefuran. We performed transcriptomic and metabolomics analysiis of 2 color morphs in D. citri. Gene expression dynamics revealed that the differentially expressed genes were predominantly involved in energy metabolism, including fatty acid metabolism, amino acid metabolism, and carbohydrate metabolism. Among these genes, plexin, glycosidase, phospholipase, take out, trypsin, and triacylglycerol lipase were differentially expressed in 2 color morphs, and 6 hsps (3 hsp70, hsp83, hsp90, hsp68) were upregulated in gray morphs. The metabolome data showed that blue morphs exhibited a higher abundance of fatty acid and amino acid, whereas the content of carbohydrates was elevated in gray morphs. This study partly explains the body color polyphenism of D. citri and provides insights into the molecular changes of stress response of D. citri.

All-natural environmentally degradable poly (butylene terephthalate-co-caprolactone): A theoretical and experimental study of its degradation properties and mechanisms.

The design and production of materials with excellent mechanical properties and biodegradability face significant challenges. Poly (butylene terephthalate-co-caprolactone) copolyesters (PBTCL) is obtained by modifying the engineering plastic polybutylene terephthalate (PBT) with a simple one-pot process using readily biodegradable ε-caprolactone (ε-CL). The material has mechanical properties comparable to those of commercial biodegradable copolyester PBAT. Besides, this copolyester exhibited remarkable degradability in natural environments such as soil and ocean, for example, PBTCL1.91 lost >40 % of its weight after 6 months of immersion in the Bohai Sea. The effect and diversity of specific microorganisms acting on degradation in the ocean were analyzed by 16 s rDNA gene sequencing. Theoretical calculations such as Fukui function and DFT, and experimental studies on water-soluble intermediates and residual matrixes produced after degradation, confirmed that the insertion CL units not only act as active sites themselves susceptible to hydrolysis reactions, but also promote the reactivity of ester bonds between aromatic segments. This work provides insight for the development of novel materials with high performance and environmental degradability.

SQANTI3: curation of long-read transcriptomes for accurate identification of known and novel isoforms.

The emergence of long-read RNA sequencing (lrRNA-seq) has provided an unprecedented opportunity to analyze transcriptomes at isoform resolution. However, the technology is not free from biases, and transcript models inferred from these data require quality control and curation. In this study, we introduce SQANTI3, a tool specifically designed to perform quality analysis on transcriptomes constructed using lrRNA-seq data. SQANTI3 provides an extensive naming framework to describe transcript model diversity in comparison to the reference transcriptome. Additionally, the tool incorporates a wide range of metrics to characterize various structural properties of transcript models, such as transcription start and end sites, splice junctions, and other structural features. These metrics can be utilized to filter out potential artifacts. Moreover, SQANTI3 includes a Rescue module that prevents the loss of known genes and transcripts exhibiting evidence of expression but displaying low-quality features. Lastly, SQANTI3 incorporates IsoAnnotLite, which enables functional annotation at the isoform level and facilitates functional iso-transcriptomics analyses. We demonstrate the versatility of SQANTI3 in analyzing different data types, isoform reconstruction pipelines, and sequencing platforms, and how it provides novel biological insights into isoform biology. The SQANTI3 software is available at https://github.com/ConesaLab/SQANTI3 .

Correction: Liu et al. Apigenin Ameliorates Hyperuricemia and Renal Injury through Regulation of Uric Acid Metabolism and JAK2/STAT3 Signaling Pathway. Pharmaceuticals 2022, 15, 1442.

In the original publication [...].

Particle size distribution measurement based on the angular scattering efficiency factor spectra inversion-simulation and experiment.

The quantification of the particle size distribution (PSD) within a particle system is significant to various domains, including atmospheric and environmental sciences, material science, civil engineering, and human health. The scattering spectrum reflects the PSD information of the particle system. Researchers have developed high-precision and high-resolution PSD measurements for monodisperse particle systems through scattering spectroscopy. However, for polydisperse particle systems, current methods based on light scattering spectrum and Fourier transform analysis can only obtain the information of the particle component, but cannot provide the relative content information of each component. In this paper, a PSD inversion method based on the angular scattering efficiency factors (ASEF) spectrum is proposed. By establishing a light energy coefficient distribution matrix, and then measuring the scattering spectrum of the particle system, PSD can be measured in conjunction with inversion algorithms. The simulations and experiments conducted in this paper substantiate the validity of the proposed method. Unlike the forward diffraction approach that measures the spatial distribution of scattered light I(θ) for inversion, our method uses the multi-wavelength distribution information of scattered light ß(λ). Moreover, the influences of noise, scattering angle, wavelength, particle size range, and size discretization interval on PSD inversion are studied. The method of condition number analysis is proposed to identify the appropriate scattering angle, particle size measurement range, and size discretization interval, and it can reduce the root mean square error(RMSE) of PSD inversion. Furthermore, the method of wavelength sensitivity analysis is proposed to select the spectral band with higher sensitivity to particle size changes, thereby improving the computational speed and avoiding the problem of diminished accuracy caused by the reduction of the number of wavelengths used.