GraphLncLoc: long non-coding RNA subcellular localization prediction using graph convolutional networks based on sequence to graph transformation.

The subcellular localization of long non-coding RNAs (lncRNAs) is crucial for understanding lncRNA functions. Most of existing lncRNA subcellular localization prediction methods use k-mer frequency features to encode lncRNA sequences. However, k-mer frequency features lose sequence order information and fail to capture sequence patterns and motifs of different lengths. In this paper, we proposed GraphLncLoc, a graph convolutional network-based deep learning model, for predicting lncRNA subcellular localization. Unlike previous studies encoding lncRNA sequences by using k-mer frequency features, GraphLncLoc transforms lncRNA sequences into de Bruijn graphs, which transforms the sequence classification problem into a graph classification problem. To extract the high-level features from the de Bruijn graph, GraphLncLoc employs graph convolutional networks to learn latent representations. Then, the high-level feature vectors derived from de Bruijn graph are fed into a fully connected layer to perform the prediction task. Extensive experiments show that GraphLncLoc achieves better performance than traditional machine learning models and existing predictors. In addition, our analyses show that transforming sequences into graphs has more distinguishable features and is more robust than k-mer frequency features. The case study shows that GraphLncLoc can uncover important motifs for nucleus subcellular localization. GraphLncLoc web server is available at http://csuligroup.com:8000/GraphLncLoc/.

A review of enzyme design in catalytic stability by artificial intelligence.

The design of enzyme catalytic stability is of great significance in medicine and industry. However, traditional methods are time-consuming and costly. Hence, a growing number of complementary computational tools have been developed, e.g. ESMFold, AlphaFold2, Rosetta, RosettaFold, FireProt, ProteinMPNN. They are proposed for algorithm-driven and data-driven enzyme design through artificial intelligence (AI) algorithms including natural language processing, machine learning, deep learning, variational autoencoder/generative adversarial network, message passing neural network (MPNN). In addition, the challenges of design of enzyme catalytic stability include insufficient structured data, large sequence search space, inaccurate quantitative prediction, low efficiency in experimental validation and a cumbersome design process. The first principle of the enzyme catalytic stability design is to treat amino acids as the basic element. By designing the sequence of an enzyme, the flexibility and stability of the structure are adjusted, thus controlling the catalytic stability of the enzyme in a specific industrial environment or in an organism. Common indicators of design goals include the change in denaturation energy (ΔΔG), melting temperature (ΔTm), optimal temperature (Topt), optimal pH (pHopt), etc. In this review, we summarized and evaluated the enzyme design in catalytic stability by AI in terms of mechanism, strategy, data, labeling, coding, prediction, testing, unit, integration and prospect.

Identifying spatial domain by adapting transcriptomics with histology through contrastive learning.

Recent advances in spatial transcriptomics have enabled measurements of gene expression at cell/spot resolution meanwhile retaining both the spatial information and the histology images of the tissues. Accurately identifying the spatial domains of spots is a vital step for various downstream tasks in spatial transcriptomics analysis. To remove noises in gene expression, several methods have been developed to combine histopathological images for data analysis of spatial transcriptomics. However, these methods either use the image only for the spatial relations for spots, or individually learn the embeddings of the gene expression and image without fully coupling the information. Here, we propose a novel method ConGI to accurately exploit spatial domains by adapting gene expression with histopathological images through contrastive learning. Specifically, we designed three contrastive loss functions within and between two modalities (the gene expression and image data) to learn the common representations. The learned representations are then used to cluster the spatial domains on both tumor and normal spatial transcriptomics datasets. ConGI was shown to outperform existing methods for the spatial domain identification. In addition, the learned representations have also been shown powerful for various downstream tasks, including trajectory inference, clustering, and visualization.

TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning.

The cellular immune system, which is a critical component of human immunity, uses T cell receptors (TCRs) to recognize antigenic proteins in the form of peptides presented by major histocompatibility complex (MHC) proteins. Accurate definition of the structural basis of TCRs and their engagement of peptide-MHCs can provide major insights into normal and aberrant immunity, and can help guide the design of vaccines and immunotherapeutics. Given the limited amount of experimentally determined TCR-peptide-MHC structures and the vast amount of TCRs within each individual as well as antigenic targets, accurate computational modeling approaches are needed. Here, we report a major update to our web server, TCRmodel, which was originally developed to model unbound TCRs from sequence, to now model TCR-peptide-MHC complexes from sequence, utilizing several adaptations of AlphaFold. This method, named TCRmodel2, allows users to submit sequences through an easy-to-use interface and shows similar or greater accuracy than AlphaFold and other methods to model TCR-peptide-MHC complexes based on benchmarking. It can generate models of complexes in 15 minutes, and output models are provided with confidence scores and an integrated molecular viewer. TCRmodel2 is available at https://tcrmodel.ibbr.umd.edu.

Myeloid-derived suppressor cells from tumour-bearing mice induce the population expansion of CD19hiFcγRIIbhi regulatory B cells via PD-L1.

Myeloid-derived suppressor cells (MDSCs) increase in number and gain immunosuppressive functions in tumours and many other pathological conditions. MDSCs are characterized by their strong T-cell immunosuppressive capacity. The effects that MDSCs may have on B cells, especially within the tumour microenvironment, are less well understood. Here, we report that either monocytic MDSCs or polymorphonuclear MDSCs can promote increases in interleukin (IL)-10-expressing CD19hiFcγRIIbhi regulatory B cells in vitro and in vivo. Splenic transitional-1, -2, and -3 cells and marginal zone B cells, but not follicular B cells, differentiate into IL-10-expressing CD19hiFcγRIIbhi regulatory B cells. The adoptive transfer of CD19hiFcγRIIbhi regulatory B cells via tail vein injection can promote subcutaneous 3LL tumour growth in mice. The expression of programmed death-ligand 1 on MDSCs was found to be strongly associated with CD19hiFcγRIIbhi regulatory B cell population expansion. Furthermore, the frequency of circulating CD19+FcγRIIhi regulatory B cells was significantly increased in advanced-stage lung cancer patients. Our results unveil a critical role of MDSCs in regulatory B-cell differentiation and population expansion in lung cancer patients.

Prostate cancer cancer-associated fibroblasts with stable markers post-androgen deprivation therapy associated with tumor progression and castration resistant prostate cancer.

The specificity and clinical relevance of cancer-associated fibroblasts (CAFs) in prostate cancer (PCa), as well as the effect of androgen deprivation therapy (ADT) on CAFs, remain to be fully elucidated. Using cell lineage diversity and weighted gene co-expression network analysis (WGCNA), we pinpointed a unique CAF signature exclusive to PCa. The specificity of this CAF signature was validated through single-cell RNA sequencing (scRNA-seq), cell line RNA sequencing, and immunohistochemistry. This signature associates CAFs with tumor progression, elevated Gleason scores, and the emergence of castration resistant prostate cancer (CRPC). Using scRNA-seq on collected samples, we demonstrated that the CAF-specific signature is not altered by ADT, maintaining its peak signal output. Identifying a PCa-specific CAF signature and observing signaling changes in CAFs after ADT lay essential groundwork for further PCa studies.

Pathogenic mechanisms of cardiovascular damage in COVID-19.

BACKGROUND: COVID-19 is a new infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). Since the outbreak in December 2019, it has caused an unprecedented world pandemic, leading to a global human health crisis. Although SARS CoV-2 mainly affects the lungs, causing interstitial pneumonia and severe acute respiratory distress syndrome, a number of patients often have extensive clinical manifestations, such as gastrointestinal symptoms, cardiovascular damage and renal dysfunction. PURPOSE: This review article discusses the pathogenic mechanisms of cardiovascular damage in COVID-19 patients and provides some useful suggestions for future clinical diagnosis, treatment and prevention. METHODS: An English-language literature search was conducted in PubMed and Web of Science databases up to 12th April, 2024 for the terms "COVID-19", "SARS CoV-2", "cardiovascular damage", "myocardial injury", "myocarditis", "hypertension", "arrhythmia", "heart failure" and "coronary heart disease", especially update articles in 2023 and 2024. Salient medical literatures regarding the cardiovascular damage of COVID-19 were selected, extracted and synthesized. RESULTS: The most common cardiovascular damage was myocarditis and pericarditis, hypertension, arrhythmia, myocardial injury and heart failure, coronary heart disease, stress cardiomyopathy, ischemic stroke, blood coagulation abnormalities, and dyslipidemia. Two important pathogenic mechanisms of the cardiovascular damage may be direct viral cytotoxicity as well as indirect hyperimmune responses of the body to SARS CoV-2 infection. CONCLUSIONS: Cardiovascular damage in COVID-19 patients is common and portends a worse prognosis. Although the underlying pathophysiological mechanisms of cardiovascular damage related to COVID-19 are not completely clear, two important pathogenic mechanisms of cardiovascular damage may be the direct damage of the SARSCoV-2 infection and the indirect hyperimmune responses.

A framework for predicting variable-length epitopes of human-adapted viruses using machine learning methods.

The coronavirus disease 2019 pandemic has alerted people of the threat caused by viruses. Vaccine is the most effective way to prevent the disease from spreading. The interaction between antibodies and antigens will clear the infectious organisms from the host. Identifying B-cell epitopes is critical in vaccine design, development of disease diagnostics and antibody production. However, traditional experimental methods to determine epitopes are time-consuming and expensive, and the predictive performance using the existing in silico methods is not satisfactory. This paper develops a general framework to predict variable-length linear B-cell epitopes specific for human-adapted viruses with machine learning approaches based on Protvec representation of peptides and physicochemical properties of amino acids. QR decomposition is incorporated during the embedding process that enables our models to handle variable-length sequences. Experimental results on large immune epitope datasets validate that our proposed model's performance is superior to the state-of-the-art methods in terms of AUROC (0.827) and AUPR (0.831) on the testing set. Moreover, sequence analysis also provides the results of the viral category for the corresponding predicted epitopes with high precision. Therefore, this framework is shown to reliably identify linear B-cell epitopes of human-adapted viruses given protein sequences and could provide assistance for potential future pandemics and epidemics.

Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks.

The rapid development of spatial transcriptomics allows the measurement of RNA abundance at a high spatial resolution, making it possible to simultaneously profile gene expression, spatial locations of cells or spots, and the corresponding hematoxylin and eosin-stained histology images. It turns promising to predict gene expression from histology images that are relatively easy and cheap to obtain. For this purpose, several methods are devised, but they have not fully captured the internal relations of the 2D vision features or spatial dependency between spots. Here, we developed Hist2ST, a deep learning-based model to predict RNA-seq expression from histology images. Around each sequenced spot, the corresponding histology image is cropped into an image patch and fed into a convolutional module to extract 2D vision features. Meanwhile, the spatial relations with the whole image and neighbored patches are captured through Transformer and graph neural network modules, respectively. These learned features are then used to predict the gene expression by following the zero-inflated negative binomial distribution. To alleviate the impact by the small spatial transcriptomics data, a self-distillation mechanism is employed for efficient learning of the model. By comprehensive tests on cancer and normal datasets, Hist2ST was shown to outperform existing methods in terms of both gene expression prediction and spatial region identification. Further pathway analyses indicated that our model could reserve biological information. Thus, Hist2ST enables generating spatial transcriptomics data from histology images for elucidating molecular signatures of tissues.

LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism.

MOTIVATION: There is mounting evidence that the subcellular localization of lncRNAs can provide valuable insights into their biological functions. In the real world of transcriptomes, lncRNAs are usually localized in multiple subcellular localizations. Furthermore, lncRNAs have specific localization patterns for different subcellular localizations. Although several computational methods have been developed to predict the subcellular localization of lncRNAs, few of them are designed for lncRNAs that have multiple subcellular localizations, and none of them take motif specificity into consideration. RESULTS: In this study, we proposed a novel deep learning model, called LncLocFormer, which uses only lncRNA sequences to predict multi-label lncRNA subcellular localization. LncLocFormer utilizes eight Transformer blocks to model long-range dependencies within the lncRNA sequence and shares information across the lncRNA sequence. To exploit the relationship between different subcellular localizations and find distinct localization patterns for different subcellular localizations, LncLocFormer employs a localization-specific attention mechanism. The results demonstrate that LncLocFormer outperforms existing state-of-the-art predictors on the hold-out test set. Furthermore, we conducted a motif analysis and found LncLocFormer can capture known motifs. Ablation studies confirmed the contribution of the localization-specific attention mechanism in improving the prediction performance. AVAILABILITY AND IMPLEMENTATION: The LncLocFormer web server is available at http://csuligroup.com:9000/LncLocFormer. The source code can be obtained from https://github.com/CSUBioGroup/LncLocFormer.

Expanding from unilateral to bilateral: A robust deep learning-based approach for predicting radiographic osteoarthritis progression.

OBJECTIVE: To develop and validate a deep learning (DL) model for predicting osteoarthritis (OA) progression based on bilateral knee joint views. METHODS: In this retrospective study, knee joints from bilateral posteroanterior knee radiographs of participants in the Osteoarthritis Initiative were analyzed. At baseline, participants were divided into testing set 1 and development set according to the different enrolled sites. The development set was further divided into a training set and a validation set in an 8:2 ratio for model development. At 48-month follow-up, eligible patients were formed testing set 2. The Bilateral Knee Neural Network (BikNet) was developed using bilateral views, with the knee to be predicted as the main view and the contralateral knee as the auxiliary view. DenseNet and ResNext were also trained and compared as the unilateral model. Two reader tests were conducted to evaluate the model's value in predicting incident OA. RESULTS: Totally 3583 participants were evaluated. The BikNet we proposed outperformed ResNext and DenseNet (all area under the curve [AUC] < 0.71, P < 0.001) with AUC values of 0.761 and 0.745 in testing sets 1 and 2, respectively. With assistance of the BikNet increased clinicians' sensitivity (from 28.1-63.2% to 42.1-68.4%) and specificity (from 57.4-83.4% to 64.1-87.5%) of incident OA prediction and improved inter-observer reliability. CONCLUSION: The DL model, constructed based on bilateral knee views, holds promise for enhancing the assessment of OA and demonstrates greater robustness during subsequent follow-up evaluations as compared with unilateral models. BikNet represents a potential tool or imaging biomarker for predicting OA progression.

MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model.

BACKGROUND: Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer (BC). The value of magnetic resonance imaging (MRI)-based tumor heterogeneity in assessing ALN metastasis in BC is unclear. PURPOSE: To assess the value of deep learning (DL)-derived kinetic heterogeneity parameters based on BC dynamic contrast-enhanced (DCE)-MRI to infer the ALN status. STUDY TYPE: Retrospective. SUBJECTS: 1256/539/153/115 patients in the training cohort, internal validation cohort, and external validation cohorts I and II, respectively. FIELD STRENGTH/SEQUENCE: 1.5 T/3.0 T, non-contrast T1-weighted spin-echo sequence imaging (T1WI), DCE-T1WI, and diffusion-weighted imaging. ASSESSMENT: Clinical pathological and MRI semantic features were obtained by reviewing histopathology and MRI reports. The segmentation of the tumor lesion on the first phase of T1WI DCE-MRI images was applied to other phases after registration. A DL architecture termed convolutional recurrent neural network (ConvRNN) was developed to generate the KHimage (kinetic heterogeneity of DCE-MRI image) score that indicated the ALN status in patients with BC. The model was trained and optimized on training and internal validation cohorts, tested on two external validation cohorts. We compared ConvRNN model with other 10 models and the subgroup analyses of tumor size, magnetic field strength, and molecular subtype were also evaluated. STATISTICAL TESTS: Chi-squared, Fisher's exact, Student's t, Mann-Whitney U tests, and receiver operating characteristics (ROC) analysis were performed. P < 0.05 was considered significant. RESULTS: The ConvRNN model achieved area under the curve (AUC) of 0.802 in the internal validation cohort and 0.785-0.806 in the external validation cohorts. The ConvRNN model could well evaluate the ALN status of the four molecular subtypes (AUC = 0.685-0.868). The patients with larger tumor sizes (>5 cm) were more susceptible to ALN metastasis with KHimage scores of 0.527-0.827. DATA CONCLUSION: A ConvRNN model outperformed traditional models for determining the ALN status in patients with BC. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

New insights into micro-algal astaxanthin's effect on deoxynivalenol-induced spleen lymphocytes pyroptosis in Cyprinus carpio: Involving mitophagy and mtROS-NF-κB-dependent NLRP3 inflammasome.

Deoxynivalenol (DON) is one of the most common sources of fungal toxins in fish feed, posing a significant risk to the immune and reproductive systems of fish. Microalgal astaxanthin (MIA), a potent antioxidant derived from microalgae, confers multifarious advantages upon piscine organisms, notably encompassing its anti-inflammatory and antioxidant prowess. Herein, we investigated the potential of MIA in ameliorating the immunotoxicity of DON on carp (Cyprinus carpio L.) based on spleen lymphocytes treated with DON (1.5 ng/ml) and/or MIA (96 µM). Firstly, CCK8 results showed that DON resulted in excessive death of spleen lymphocytes. Secondly, spleen lymphocytes treated with DON had a higher proportion of pyroptosis, and the mRNA and protein levels of pyroptosis (NLRP3, IL-1ß and ASC) in spleen lymphocytes were increased. Thirdly, the relative red fluorescence intensity of JC-1 and DCFH-DA showed decreased mitochondrial membrane potential and increased ROS in spleen lymphocytes treated with DON. Mitochondrial ATP, DNA and NADPH/NADP+ analysis revealed decreased mitochondrial ATP, DNA and NADPH/NADP+ levels in DON-treated lymphocytes, corroborating the association between DON exposure and elevated intracellular ATP, DNA and NADPH/NADP+ in lymphocytes. DON exposure resulted in the downregulation of mitophagy-related genes and proteins (PINK1, Parkin and LC3) in lymphocytes. Notably, these effects were counteracted by treatment with MIA. Furthermore, DON led to the elevated secretion of inflammatory factors (TNF-α, IL-4 and IFN-γ), thereby inducing immune dysfunction in spleen lymphocytes. Encouragingly, MIA treatment effectively mitigated the immunotoxic effects induced by DON, demonstrating its potential in ameliorating pyroptosis, mitochondrial dysfunction and mitophagy impairment via regulating the mtROS-NF-κB axis in lymphocytes. This study sheds light on safeguarding farmed fish against agrobiological threats posed by DON, highlighting the valuable applications of MIA in aquaculture.

Visualized Enzyme-Activated Fluorescence Probe for Accurately Detecting ß-Gal in Living Cells and BALB/c Nude Mice.

Colorectal cancer was one of the major malignant tumors threatening human health and ß-Gal was recognized as a principal biomarker for primary colorectal cancer. Thus, designing specific and efficient quantitative detection methods for measuring ß-Gal enzyme activity was of great clinical test significance. Herein, an ultrasensitive detection method based on Turn-on fluorescence probe (CS-ßGal) was reported for visualizing the detection of exogenous and endogenous ß-galactosidase enzyme activity. The test method possessed a series of excellent performances, such as a significant fluorescence enhancement (about 11.3-fold), high selectivity as well as superior sensitivity. Furthermore, under the optimal experimental conditions, a relatively low limit of detection down to 0.024 U/mL was achieved for fluorescence titration experiment. It was thanks to the better biocompatibility and low cytotoxicity, CS-ßGal had been triumphantly employed to visual detect endogenous and exogenous ß-Gal concentration variations in living cells with noteworthy anti-interference performance. More biologically significant was the fact that the application of CS-ßGal in BALB/c nude mice was also achieved successfully for monitoring endogenous ß-Gal enzyme activity.

Transformer-based Multi-label Deep Learning Model is Efficient for Detecting Ankle Lateral and Medial Ligament Injuries on MRI and Improving Clinicians' Diagnostic Accuracy for Rotational Chronic Ankle Instability.

PURPOSE: To develop a deep learning (DL) model that can simultaneously detect lateral and medial collateral ligament injuries of the ankle, aiding in the diagnosis of chronic ankle instability (CAI), and assess its impact on clinicians' diagnostic performance. METHODS: DL models were developed and external validated on retrospectively collected ankle MRIs between April 2016 and March 2022 respectively at three centers. Included patients were confirmed diagnoses of CAI through arthroscopy, as well as individuals who had undergone MRI and physical examinations that ruled out ligament injuries. DL models were constructed based on a multi-label paradigm. A transformer-based multi-label DL model (AnkleNet) was developed and compared with four convolution neural network (CNN) models. Subsequently, a reader study was conducted to evaluate the impact of model assistance on clinicians when diagnosing challenging cases: identifying rotational CAI (RCAI). Diagnostic performance was assessed using area under the receiver operating characteristic curve (AUC). RESULTS: Our transformer-based model achieved AUC of 0.910 and 0.892 for detecting lateral and medial collateral ligament injury, respectively, both of which was significantly higher than that of CNN-based models (all P < 0.001). In terms of further CAI diagnosis, it exhibited a macro-average AUC of 0.870 and a balanced accuracy of 0.805. The reader study indicated that incorporation with our model significantly enhanced the diagnostic accuracy of clinicians (P = 0.042), particularly junior clinicians, and led to a reduction in diagnostic variability. The code of the model can be accessed at https://github.com/ChiariRay/AnkleNet. CONCLUSION: Our transformer-based model was able to detect lateral and medial collateral ligament injuries based on MRI and outperformed CNN-based models, demonstrating a promising performance in diagnosing CAI, especially RCAI patients.

EF-Net: Mental State Recognition by Analyzing Multimodal EEG-fNIRS via CNN.

Analysis of brain signals is essential to the study of mental states and various neurological conditions. The two most prevalent noninvasive signals for measuring brain activities are electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). EEG, characterized by its higher sampling frequency, captures more temporal features, while fNIRS, with a greater number of channels, provides richer spatial information. Although a few previous studies have explored the use of multimodal deep-learning models to analyze brain activity for both EEG and fNIRS, subject-independent training-testing split analysis remains underexplored. The results of the subject-independent setting directly show the model's ability on unseen subjects, which is crucial for real-world applications. In this paper, we introduce EF-Net, a new CNN-based multimodal deep-learning model. We evaluate EF-Net on an EEG-fNIRS word generation (WG) dataset on the mental state recognition task, primarily focusing on the subject-independent setting. For completeness, we report results in the subject-dependent and subject-semidependent settings as well. We compare our model with five baseline approaches, including three traditional machine learning methods and two deep learning methods. EF-Net demonstrates superior performance in both accuracy and F1 score, surpassing these baselines. Our model achieves F1 scores of 99.36%, 98.31%, and 65.05% in the subject-dependent, subject-semidependent, and subject-independent settings, respectively, surpassing the best baseline F1 scores by 1.83%, 4.34%, and 2.13% These results highlight EF-Net's capability to effectively learn and interpret mental states and brain activity across different and unseen subjects.

Hierarchically Porous Few-Layer Carbon Nitride and Its High H+ Selectivity for Efficient Photocatalytic Seawater Splitting.

Photocatalysts for seawater splitting are severely restricted because of the presence of multiple types of ions in seawater that cause corrosion and deactivation. As a result, new materials that promote adsorption of H+ and hinder competing adsorption of metal cations should enhance utilization of photogenerated electrons on the catalyst surface for efficient H2 production. One strategy to design advanced photocatalysts involves introduction of hierarchical porous structures that enable fast mass transfer and creation of defect sites that promote selective hydrogen ion adsorption. Herein, we used a facile calcination method to fabricate the macro-mesoporous C3N4 derivative, VN-HCN, that contains multiple nitrogen vacancies. We demonstrated that VN-HCN has enhanced corrosion resistance and elevated photocatalytic H2 production performance in seawater. Experimental results and theoretical calculations reveal that enhanced mass and carrier transfer and selective adsorption of hydrogen ions are key features of VN-HCN that lead to its high seawater splitting activity.

Variations and patterns of C and N stoichiometry in the first five root branch orders across 218 woody plant species.

Despite the vital role in carbon (C) sequestration and nutrient retention, variations and patterns in root C and nitrogen (N) stoichiometry of the first five root orders across woody plant species remains unclear. We compiled a dataset to explore variations and patterns of root C and N stoichiometry in the first five orders of 218 woody plant species. Across the five orders, root N concentrations were greater in deciduous, broadleaf, and arbuscular mycorrhizal species than in evergreen, coniferous species, and ectomycorrhizal association species, respectively. Contrasting trends were found for root C : N ratios. Most root branch orders showed clear latitudinal and altitudinal trends in root C and N stoichiometry. There were opposite patterns in N concentrations between latitude and altitude. Such variations were mainly driven by plant species, and climatic factors together. Our results indicate divergent C and N use strategies among plant types and convergence and divergence in the patterns of C and N stoichiometry between latitude and altitude across the first five root orders. These findings provide important data on the root economics spectrum and biogeochemical models to improve understanding and prediction of climate change effects on C and nutrient dynamics in terrestrial ecosystems.

Polarizer-free measurement of the full Stokes vector using a fiber-coupled superconducting nanowire single photon detector with a polarization extinction ratio of ∼2.

The characterization and manipulation of polarization state at single photon level are of great importance in research fields such as quantum information processing and quantum key distribution, where photons are normally delivered using single mode optical fibers. To date, the demonstrated polarimetry measurement techniques based on a superconducting nanowire single photon detector (SNSPD) require the SNSPD to be either highly sensitive or highly insensitive to the photon's polarization state, therefore placing an unavoidable challenge on the SNSPD's design and fabrication processes. In this article, we present the development of an alternative polarimetry measurement technique, of which the stringent requirement on the SNSPD's polarization sensitivity is removed. We validate the proposed technique by a rigorous theoretical analysis and comparisons of the experimental results obtained using a fiber-coupled SNSPD with a polarization extinction ratio of ∼2 to that obtained using other well-established known methods. Based on the full Stokes data measured by the proposed technique, we also demonstrate that at the single photon level (∼ -100 dBm), the polarization state of the photon delivered to the superconducting nanowire facet plane can be controlled at will using a further developed algorithm. Note that other than the fiber-coupled SNSPD, the only component involved is a quarter-wave plate (no external polarizer is necessary), which when aligned well has a paid insertion loss less than 0.5 dB.

PGAM4 silencing inhibited glycolysis and chemoresistance to temozolomide in glioma cells.

Gliomas account for about 80% of malignant brain tumors. The incidence of a new brain tumor is 6.4 per 100,000 persons per year with an overall 5-year survival rate of 33.4%. Regardless of the great advances that have been made in recent years, the causes and pathogenesis of glioma remain unclear. Here we study how phosphoglycerate mutase 4 (PGAM4) contributes to glioma. Using a variety of methods to examine glioma cell viability, proliferation, apoptosis, glycolysis, as well as ChIP coanalysis with modified histone H3, we showed that PGAM4 was significantly upregulated in patients with glioma and associated with poor survival. Silencing PGAM4 attenuated cell viability, proliferation, and glycolysis in T98G cells and suppressed tumor growth in vivo, while overexpressing PGAM4 promoted cell viability, proliferation, and glycolysis in U251 cells via regulating glycolysis pathway. Study also revealed that PGAM4 was regulated by EP300-mediated modifications of H3K27ac. PGAM4 silencing inhibited cell viability and proliferation, suppressed tumor growth, and decreased chemoresistance to temozolomide in glioma cells through suppressing glycolysis.