CanCellVar: A database for single-cell variants map in human cancer.

Numerous variants, including both single-nucleotide variants (SNVs) in DNA and A>G RNA edits in mRNA as essential drivers of cellular proliferation and tumorigenesis, are commonly associated with cancer progression and growth. Thus, mining and summarizing single-cell variants will provide a refined and higher-resolution view of cancer and further contribute to precision medicine. Here, we established a database, CanCellVar, which aims to provide and visualize the comprehensive atlas of single-cell variants in tumor microenvironment. The current CanCellVar identified ∼3 million variants (∼1.4 million SNVs and ∼1.4 million A>G RNA edits) involved in 2,754,531 cells of 5 major cell types across 37 cancer types. CanCellVar provides the basic annotation information as well as cellular and molecular function properties of variants. In addition, the clinical relevance of variants can be obtained including tumor grade, treatment, metastasis, and others. Several flexible tools were also developed to aid retrieval and to analyze cell-cell interactions, gene expression, cell-development trajectories, regulation, and molecular structure affected by variants. Collectively, CanCellVar will serve as a valuable resource for investigating the functions and characteristics of single-cell variations and their roles in human tumor evolution and treatment.

Interaction of widowhood, gender, and age in predicting loneliness among older adults in China.

BACKGROUND: Loneliness is a significant issue for the elderly, and widowhood is considered a major risk factor. However, research on the intersectional effects of gender, age, and widowhood on loneliness is limited, especially within the Chinese cultural context. METHODS: Using six waves (2002-2018) of national longitudinal data from the Chinese Longitudinal Healthy Longevity Survey (N = 22,777), this study employed multilevel mixed-effects ordered logistic regression to analyze the impact of widowhood on loneliness. Moderating roles of gender and age were examined through interaction effects. RESULTS: Widowhood significantly increased loneliness across genders and age groups, but this effect diminished with age. Widowed men experienced greater loneliness than women, but this difference converged by age 90. The buffering effect of age on the widowhood-loneliness link was less pronounced among older women. CONCLUSION: The study unravels the complexity of how gender, age, and widowhood interact to shape loneliness in later life. Targeted interventions considering these intersections are needed to alleviate loneliness among Chinese widowed elderly.

A new Graph Gaussian embedding method for analyzing the effects of cognitive training.

Identifying heterogeneous cognitive impairment markers at an early stage is vital for Alzheimer's disease diagnosis. However, due to complex and uncertain brain connectivity features in the cognitive domains, it remains challenging to quantify functional brain connectomic changes during non-pharmacological interventions for amnestic mild cognitive impairment (aMCI) patients. We present a quantitative method for functional brain network analysis of fMRI data based on the multi-graph unsupervised Gaussian embedding method (MG2G). This neural network-based model can effectively learn low-dimensional Gaussian distributions from the original high-dimensional sparse functional brain networks, quantify uncertainties in link prediction, and discover the intrinsic dimensionality of brain networks. Using the Wasserstein distance to measure probabilistic changes, we discovered that brain regions in the default mode network and somatosensory/somatomotor hand, fronto-parietal task control, memory retrieval, and visual and dorsal attention systems had relatively large variations during non-pharmacological training, which might provide distinct biomarkers for fine-grained monitoring of aMCI cognitive alteration. An important finding of our study is the ability of the new method to capture subtle changes for individual patients before and after short-term intervention. More broadly, the MG2G method can be used in studying multiple brain disorders and injuries, e.g., in Parkinson's disease or traumatic brain injury (TBI), and hence it will be useful to the wider neuroscience community.

Dual cascade isothermal amplification reaction based glucometer sensors for point-of-care diagnostics of cancer-related microRNAs.

The practical use of a point-of-care (POC) device is of particular interest in performing liquid biopsies related to cancer. Herein, taking advantage of the practical convenience of a commercially available personal glucose meter (PGM), we report a convenient, low-cost and sensitive detection strategy for circulating microRNA-155 (miRNA155) in human serum. First, miRNA155 in serum triggers the catalyzed hairpin assembly (CHA) reaction, and then the CHA product is specifically captured by the peptide nucleic acid (PNA) probes attached to the surface of a 96-well plate, which in turn triggers the hybridization chain reaction (HCR), resulting in the local enrichment of invertase. Next, introduction of a substrate (sucrose) for the invertase results in the generation of glucose, which can be detected by a PGM. In this sensor, neutrally charged PNA (12 nt) is more likely to hybridize with the CHA products than with the negatively charged DNA in kinetics, which improves the detection sensitivity and specificity. Due to the synergistic isothermal amplification reaction between CHA and HCR, the sensor is able to achieve a broad dynamic range (from 1 fM to 10 nM) with a detection limit down to 0.36 fM (3 orders of magnitude lower than that without HCR) and is capable of distinguishing single-base mismatched sequences. Thus the convenient, sensitive, robust and low-cost PGM sensor makes on-site nucleic acids detection possible, suggesting its great application prospect as a promising POC device in cancer diagnostics.

Age and cohort trajectories of gender inequality in health among elderly people in China.

Using a multistate model, this article examines gender gap and its trend in health transition, life expectancy (LE), and active life expectancy (ALE) in different age groups and birth cohorts. The transition rate from life independence/disability to death for elderly women is lower than for elderly men. However, their disability rate is higher. The gender gap in LE, ALE, and DLE (disabled life expectancy) declines as age increases. In successive birth cohorts, men's LE increases more, and gender gaps in LE and ALE decrease over time. In the future, gender issues should be considered for health-care policy to provide nursing care for elderly women in China.

A PNA-DNA2 Triple-Helix Molecular Switch-Based Colorimetric Sensor for Sensitive and Specific Detection of microRNAs from Cancer Cells.

Peptide nucleic acids (PNAs), the synthetic DNA mimics that can bind to oligonucleotides to form duplexes, triplexes, and quadruplexes, could be advantageous as probes for nucleic acid sequences owing to their unique physicochemical and biochemical properties. We have found that a homopurine PNA strand could bind to two homopyrimidine DNA strands to form a PNA-DNA2 triplex. Moreover, the cyanine dye DiSC2 (5) could bind with high affinity to this triplex and cause a noticeable color change. On the basis of this phenomenon, we have designed a label-free colorimetric sensing platform for miRNAs from cancer cells by using a PNA-DNA2 triple-helix molecular switch (THMS) and DiSC2 (5). This sensing platform can detect miRNA-21 specifically with a detection limit of 0.18 nM, which is comparable to that of the THMS-mediated fluorescence sensing platform. Moreover, this colorimetric platform does not involve any chemical modification or enzymatic signal amplification, which boosts its applicability and availability at the point of care in resource-limited settings. The universality of this approach can be simply achieved by altering the sequences of the probe DNA for specific targets.

A peptide nucleic acid-regulated fluorescence resonance energy transfer DNA assay based on the use of carbon dots and gold nanoparticles.

A convenient fluorometric method was developed for specific determination of DNA based on peptide nuclei acid (PNA)-regulated fluorescence resonance energy transfer (FRET) between carbon dots (CDs) and gold nanoparticles (AuNPs). In this system, CDs that display lake blue fluorescence with excitation/emission maxima at 345/445 nm were used as fluorometric reporter, while AuNPs were used as fluorescence nanoquencher. A neutral PNA probe, which is designed to recognize the target DNA, was used as a coagulant to control the dispersion and aggregation of AuNPs. Without DNA, PNA can induce immediate AuNP aggregation, thus leading to the recovery of the FRET-quenched fluorescence emission of CDs. However, the addition of the complementary target DNA can protect AuNPs from being aggregated due to the formation of DNA/PNA complexes, which subsequently produces a high fluorescence quenching efficiency of CDs by dispersed AuNPs. Under optimized conditions, quantitative evaluation of DNA was achieved in a linear range of 5-100 nM with a detection limit of 0.21 nM. This method exhibited an excellent specificity towards fully matched DNA. In addition, the application of this assay for sensitive determination of DNA in cell lysate demonstrates its potential for bioanalysis and biodetection. Graphical abstract A simple fluorometric biosensor for specific detection of DNA was developed based on peptide nuclei acid (PNA)-regulated fluorescence resonance energy transfer (FRET) between carbon dots (CDs) and gold nanoparticles (AuNPs).

The establishment of Chinese ecological conservation redline and insights into improving international protected areas.

China proposed the Ecological Conservation Redline (ECR) strategy in 2011 and listed it as an important part of the ecological promotion process in 2013. In 2017, China implemented the designation of ECR at a national scale. The aim was to demarcate areas with unique ecological functions. Therefore, the ECR became a key part of national ecological security. Three major types of area are covered by the ECR so that important ecosystem functions can be protected. These are ecologically fragile zones, biodiversity, and natural landscapes. The ECR is characterized as being fundamental, systematic, and mandatory. This study summarizes the developmental background, theoretical framework, and structural composition of ECR, and highlights recent advances. A comparison of the ECR to international protected areas showed that they were generally similar, but there were differences in the methods used to protect targets, their system design, and their management, which provided insights and recommendations that could improve the construction and management of protected areas in the future. These included: (1) increasing the integrity of ecological protection by replacing dispersed protection with a more comprehensive and systematic approach, (2) increasing the spatial accuracy of protection to enhance the effectiveness of protection, and (3) implementing an overall-process management to guarantee the sustainability of protection. Finally, suggestions are made for further protected area improvements and ecological protection, including further theoretical exploration, strengthening management, and facilitating international cooperation.

The light chains of kinesin-1 are autoinhibited.

The light chains (KLCs) of the microtubule motor kinesin-1 bind cargoes and regulate its activity. Through their tetratricopeptide repeat domain (KLC(TPR)), they can recognize short linear peptide motifs found in many cargo proteins characterized by a central tryptophan flanked by aspartic/glutamic acid residues (W-acidic). Using a fluorescence resonance energy transfer biosensor in combination with X-ray crystallographic, biochemical, and biophysical approaches, we describe how an intramolecular interaction between the KLC2(TPR) domain and a conserved peptide motif within an unstructured region of the molecule, partly occludes the W-acidic binding site on the TPR domain. Cargo binding displaces this interaction, effecting a global conformational change in KLCs resulting in a more extended conformation. Thus, like the motor-bearing kinesin heavy chains, KLCs exist in a dynamic conformational state that is regulated by self-interaction and cargo binding. We propose a model by which, via this molecular switch, W-acidic cargo binding regulates the activity of the holoenzyme.

A deep convolutional neural network for classification of red blood cells in sickle cell anemia.

Sickle cell disease (SCD) is a hematological disorder leading to blood vessel occlusion accompanied by painful episodes and even death. Red blood cells (RBCs) of SCD patients have diverse shapes that reveal important biomechanical and bio-rheological characteristics, e.g. their density, fragility, adhesive properties, etc. Hence, having an objective and effective way of RBC shape quantification and classification will lead to better insights and eventual better prognosis of the disease. To this end, we have developed an automated, high-throughput, ex-vivo RBC shape classification framework that consists of three stages. First, we present an automatic hierarchical RBC extraction method to detect the RBC region (ROI) from the background, and then separate touching RBCs in the ROI images by applying an improved random walk method based on automatic seed generation. Second, we apply a mask-based RBC patch-size normalization method to normalize the variant size of segmented single RBC patches into uniform size. Third, we employ deep convolutional neural networks (CNNs) to realize RBC classification; the alternating convolution and pooling operations can deal with non-linear and complex patterns. Furthermore, we investigate the specific shape factor quantification for the classified RBC image data in order to develop a general multiscale shape analysis. We perform several experiments on raw microscopy image datasets from 8 SCD patients (over 7,000 single RBC images) through a 5-fold cross validation method both for oxygenated and deoxygenated RBCs. We demonstrate that the proposed framework can successfully classify sickle shape RBCs in an automated manner with high accuracy, and we also provide the corresponding shape factor analysis, which can be used synergistically with the CNN analysis for more robust predictions. Moreover, the trained deep CNN exhibits good performance even for a deoxygenated dataset and distinguishes the subtle differences in texture alteration inside the oxygenated and deoxygenated RBCs.

Health status and air pollution related socioeconomic concerns in urban China.

BACKGROUND: China is experiencing environmental issues and related health effects due to its industrialization and urbanization. The health effects associated with air pollution are not just a matter of epidemiology and environmental science research, but also an important social science issue. Literature about the relationship of socioeconomic factors with the environment and health factors is inadequate. The relationship between air pollution exposure and health effects in China was investigated with consideration of the socioeconomic factors. METHODS: Based on nationwide survey data of China in 2014, we applied the multilevel mixed-effects model to evaluate how socioeconomic status (represented by education and income) contributed to the relationship between self-rated air pollution and self-rated health status at community level and individual level. RESULTS: The findings indicated that there was a non-linear relationship between the community socioeconomic status and community air pollution in urban China, with the highest level of air pollution presented in the communities with moderate socioeconomic status. In addition, health effects associated air pollution in different socioeconomic status groups were not equal. Self-rated air pollution had the greatest impact on self-rated health of the lower socioeconomic groups. With the increase of socioeconomic status, the effect of self-rated air pollution on self-rated health decreased. CONCLUSIONS: This study verified the different levels of exposure to air pollution and inequality in health effects among different socioeconomic groups in China. It is imperative for the government to urgently formulate public policies to enhance the ability of the lower socioeconomic groups to circumvent air pollution and reduce the health damage caused by air pollution.

Generalized permutation entropy analysis based on the two-index entropic form Sq,δ.

Permutation entropy (PE) is a novel measure to quantify the complexity of nonlinear time series. In this paper, we propose a generalized permutation entropy ( PEq,δ) based on the recently postulated entropic form, Sq,δ, which was proposed as an unification of the well-known Sq of nonextensive-statistical mechanics and Sδ, a possibly appropriate candidate for the black-hole entropy. We find that PEq,δ with appropriate parameters can amplify minor changes and trends of complexities in comparison to PE. Experiments with this generalized permutation entropy method are performed with both synthetic and stock data showing its power. Results show that PEq,δ is an exponential function of q and the power ( k(δ)) is a constant if δ is determined. Some discussions about k(δ) are provided. Besides, we also find some interesting results about power law.

TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers.

Dynamic graph embedding has emerged as a very effective technique for addressing diverse temporal graph analytic tasks (i.e., link prediction, node classification, recommender systems, anomaly detection, and graph generation) in various applications. Such temporal graphs exhibit heterogeneous transient dynamics, varying time intervals, and highly evolving node features throughout their evolution. Hence, incorporating long-range dependencies from the historical graph context plays a crucial role in accurately learning their temporal dynamics. In this paper, we develop a graph embedding model with uncertainty quantification, TransformerG2G, by exploiting the advanced transformer encoder to first learn intermediate node representations from its current state (t) and previous context (over timestamps [t-1,t-l], l is the length of context). Moreover, we employ two projection layers to generate lower-dimensional multivariate Gaussian distributions as each node's latent embedding at timestamp t. We consider diverse benchmarks with varying levels of "novelty" as measured by the TEA (Temporal Edge Appearance) plots. Our experiments demonstrate that the proposed TransformerG2G model outperforms conventional multi-step methods and our prior work (DynG2G) in terms of both link prediction accuracy and computational efficiency, especially for high degree of novelty. Furthermore, the learned time-dependent attention weights across multiple graph snapshots reveal the development of an automatic adaptive time stepping enabled by the transformer. Importantly, by examining the attention weights, we can uncover temporal dependencies, identify influential elements, and gain insights into the complex interactions within the graph structure. For example, we identified a strong correlation between attention weights and node degree at the various stages of the graph topology evolution.

A framework of computer vision-enhanced microfluidic approach for automated assessment of the transient sickling kinetics in sickle red blood cells.

The occurrence of vaso-occlusive crisis greatly depends on the competition between the sickling delay time and the transit time of individual sickle cells, i.e., red blood cells (RBCs) from sickle cell disease (SCD) patients, while they are traversing the circulatory system. Many drugs for treating SCD work by inhibiting the polymerization of sickle hemoglobin (HbS), effectively delaying the sickling process in sickle cells (SS RBCs). Most previous studies on screening anti-sickling drugs, such as voxelotor, rely on in vitro testing of sickling characteristics, often conducted under prolonged deoxygenation for up to 1 hour. However, since the microcirculation of RBCs typically takes less than 1 minute, the results of these studies may be less accurate and less relevant for in vitro-in vivo correlation. In our current study, we introduce a computer vision-enhanced microfluidic framework designed to automatically capture the transient sickling kinetics of SS RBCs within a 1-min timeframe. Our study has successfully detected differences in the transient sickling kinetics between vehicle control and voxelotor-treated SS RBCs. This approach has the potential for broader applications in screening anti-sickling therapies.

Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline.

An expansive area of research focuses on discerning patterns of alterations in functional brain networks from the early stages of Alzheimer's disease, even at the subjective cognitive decline (SCD) stage. Here, we developed a novel hyperbolic MEG brain network embedding framework for transforming high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations. Using this model, we computed hyperbolic embeddings of the MEG brain networks of two distinct participant groups: individuals with SCD and healthy controls. We demonstrated that these embeddings preserve both local and global geometric information, presenting reduced distortion compared to rival models, even when brain networks are mapped into low-dimensional spaces. In addition, our findings showed that the hyperbolic embeddings encompass unique SCD-related information that improves the discriminatory power above and beyond that of connectivity features alone. Notably, we introduced a unique metric-the radius of the node embeddings-which effectively proxies the hierarchical organization of the brain. Using this metric, we identified subtle hierarchy organizational differences between the two participant groups, suggesting increased hierarchy in the dorsal attention, frontoparietal, and ventral attention subnetworks among the SCD group. Last, we assessed the correlation between these hierarchical variations and cognitive assessment scores, revealing associations with diminished performance across multiple cognitive evaluations in the SCD group. Overall, this study presents the first evaluation of hyperbolic embeddings of MEG brain networks, offering novel insights into brain organization, cognitive decline, and potential diagnostic avenues of Alzheimer's disease.

Heteroatom Introduction to Reconstruct Interfacial Chemical Structures for High-Reliability CFRTP/A6061-T6 Hybrid Structures.

The application of carbon-fiber-reinforced thermoplastic (CFRTP)/metal hybrid structures is a vital step for realizing the lightweight design concepts in aerospace. However, the CFRTP/metal hybrid structures are usually not reliable enough in practical applications due to the high differences in chemical and physical properties between these two materials. The current work provides a bottom-up strategy of introducing heteroatoms into CFRTP/metal interfaces to reconstruct the interfacial chemical structures and thus manufacture high-reliability hybrid structures. Based on the principle of utmost using reaction sites at metal surfaces, the heteroatoms of oxygen and hydrogen are specially designed and introduced to the CFRTP/A6061-T6 (6061) interfaces by simple and green plasma polymerization. The introduced oxygen and hydrogen heteroatoms react with the aluminum and oxygen of the oxidation film at 6061 surfaces to produce great interfacial Al-O covalencies and hydrogen bonds. The reconstructing interfacial chemical structures strengthen the joint strength of CFRTP/6061 hybrid structures from 8.82 to 23.97 MPa. Our heteroatom introduction strategy is expected to get a fresh insight into the interfacial design concept and has several important implications for the future application of high-reliability CFRTP/metal hybrid structures.

Determination of Isoflavones in Radix puerariae from Different Origins by Ultra-High Performance Liquid Chromatography Based on Optimal Pretreatment Method.

A new method for simultaneous determination of puerarin, daidzin, daidzein and genistein in Radix puerariae by ultra-high performance liquid chromatography was established. The target analytes were extracted from Radix puerariae by 70% ethylene glycol with the assistance of ultrasonication, purified by the absorption of N-propyl ethylenediamine (PSA), and separated on a Supersil ODS column (4.6 mm × 250 mm × 2.5 µm). Gradient elution in 12 min was performed with the mobile phase 0.1% formic acid(A)-acetonitrile(B). The column temperature was 25 °C and the flow rate was 1 mL/min. The detection wavelength of the four target analytes was 250 nm. The limits of detection (LODs) of puerarin, daidzin, daidzein and genistein were 0.086 mg/L, 0.020 mg/L, 0.027 mg/L and 0.037 mg/L, respectively, and limits of quantitation (LOQs) were 0.29 mg/L, 0.065 mg/L, 0.090 mg/L and 0.12 mg/L, respectively. The recovery of the four substances ranged from 90.5% to 109.6%, and the relative standard deviation (n = 6) was less than 7.7%. With the established methods, puerarin, daidzin, daidzein and genistein in Radix puerariae from 11 origins were determined. The contents of the four compounds varied with the origin and variety. It provides basic data and technical means for quality control and regulation of Radix puerariae.

A two-stage framework for optical coherence tomography angiography image quality improvement.

Introduction: Optical Coherence Tomography Angiography (OCTA) is a new non-invasive imaging modality that gains increasing popularity for the observation of the microvasculatures in the retina and the conjunctiva, assisting clinical diagnosis and treatment planning. However, poor imaging quality, such as stripe artifacts and low contrast, is common in the acquired OCTA and in particular Anterior Segment OCTA (AS-OCTA) due to eye microtremor and poor illumination conditions. These issues lead to incomplete vasculature maps that in turn makes it hard to make accurate interpretation and subsequent diagnosis. Methods: In this work, we propose a two-stage framework that comprises a de-striping stage and a re-enhancing stage, with aims to remove stripe noise and to enhance blood vessel structure from the background. We introduce a new de-striping objective function in a Stripe Removal Net (SR-Net) to suppress the stripe noise in the original image. The vasculatures in acquired AS-OCTA images usually exhibit poor contrast, so we use a Perceptual Structure Generative Adversarial Network (PS-GAN) to enhance the de-striped AS-OCTA image in the re-enhancing stage, which combined cyclic perceptual loss with structure loss to achieve further image quality improvement. Results and discussion: To evaluate the effectiveness of the proposed method, we apply the proposed framework to two synthetic OCTA datasets and a real AS-OCTA dataset. Our results show that the proposed framework yields a promising enhancement performance, which enables both conventional and deep learning-based vessel segmentation methods to produce improved results after enhancement of both retina and AS-OCTA modalities.

Dynamics in Deep Classifiers Trained with the Square Loss: Normalization, Low Rank, Neural Collapse, and Generalization Bounds.

We overview several properties-old and new-of training overparameterized deep networks under the square loss. We first consider a model of the dynamics of gradient flow under the square loss in deep homogeneous rectified linear unit networks. We study the convergence to a solution with the absolute minimum ρ, which is the product of the Frobenius norms of each layer weight matrix, when normalization by Lagrange multipliers is used together with weight decay under different forms of gradient descent. A main property of the minimizers that bound their expected error for a specific network architecture is ρ. In particular, we derive novel norm-based bounds for convolutional layers that are orders of magnitude better than classical bounds for dense networks. Next, we prove that quasi-interpolating solutions obtained by stochastic gradient descent in the presence of weight decay have a bias toward low-rank weight matrices, which should improve generalization. The same analysis predicts the existence of an inherent stochastic gradient descent noise for deep networks. In both cases, we verify our predictions experimentally. We then predict neural collapse and its properties without any specific assumption-unlike other published proofs. Our analysis supports the idea that the advantage of deep networks relative to other classifiers is greater for problems that are appropriate for sparse deep architectures such as convolutional neural networks. The reason is that compositionally sparse target functions can be approximated well by "sparse" deep networks without incurring in the curse of dimensionality.

Microstructure-driven electrical conductivity optimization in additively manufactured microscale copper interconnects.

As the microelectronics field pushes to increase device density through downscaling component dimensions, various novel micro- and nano-scale additive manufacturing technologies have emerged to expand the small scale design space. These techniques offer unprecedented freedom in designing 3D circuitry but have not yet delivered device-grade materials. To highlight the complex role of processing on the quality and microstructure of AM metals, we report the electrical properties of micrometer-scale copper interconnects fabricated by Fluid Force Microscopy (FluidFM) and Electrohydrodynamic-Redox Printing (EHD-RP). Using a thin film-based 4-terminal testing chip developed for the scope of this study, the electrical resistance of as-printed metals is directly related to print strategies and the specific morphological and microstructural features. Notably, the chip requires direct synthesis of conductive structures on an insulating substrate, which is shown for the first time in the case of FluidFM. Finally, we demonstrate the unique ability of EHD-RP to tune the materials resistivity by one order of magnitude solely through printing voltage. Through its novel electrical characterization approach, this study offers unique insight into the electrical properties of micro- and submicrometer-sized copper interconnects and steps towards a deeper understanding of micro AM metal properties for advanced electronics applications.