scDAC: deep adaptive clustering of single-cell transcriptomic data with coupled autoencoder and Dirichlet process mixture model.

MOTIVATION: Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is an important step in revealing cellular heterogeneity. Many clustering methods have been proposed to discover heterogenous cell types from scRNA-seq data. However, adaptive clustering with accurate cluster number reflecting intrinsic biology nature from large-scale scRNA-seq data remains quite challenging. RESULTS: Here, we propose a single-cell Deep Adaptive Clustering (scDAC) model by coupling the Autoencoder (AE) and the Dirichlet Process Mixture Model (DPMM). By jointly optimizing the model parameters of AE and DPMM, scDAC achieves adaptive clustering with accurate cluster numbers on scRNA-seq data. We verify the performance of scDAC on five subsampled datasets with different numbers of cell types and compare it with 15 widely used clustering methods across nine scRNA-seq datasets. Our results demonstrate that scDAC can adaptively find accurate numbers of cell types or subtypes and outperforms other methods. Moreover, the performance of scDAC is robust to hyperparameter changes. AVAILABILITY AND IMPLEMENTATION: The scDAC is implemented in Python. The source code is available at https://github.com/labomics/scDAC.

MetaLogo: a heterogeneity-aware sequence logo generator and aligner.

Sequence logos are used to visually display conservations and variations in short sequences. They can indicate the fixed patterns or conserved motifs in a batch of DNA or protein sequences. However, most of the popular sequence logo generators are based on the assumption that all the input sequences are from the same homologous group, which will lead to an overlook of the heterogeneity among the sequences during the sequence logo making process. Heterogeneous groups of sequences may represent clades of different evolutionary origins, or genes families with different functions. Therefore, it is essential to divide the sequences into different phylogenetic or functional groups to reveal their specific sequence motifs and conservation patterns. To solve these problems, we developed MetaLogo, which can automatically cluster the input sequences after multiple sequence alignment and phylogenetic tree construction, and then output sequence logos for multiple groups and aligned them in one figure. User-defined grouping is also supported by MetaLogo to allow users to investigate functional motifs in a more delicate and dynamic perspective. MetaLogo can highlight both the homologous and nonhomologous sites among sequences. MetaLogo can also be used to annotate the evolutionary positions and gene functions of unknown sequences, together with their local sequence characteristics. We provide users a public MetaLogo web server (http://metalogo.omicsnet.org), a standalone Python package (https://github.com/labomics/MetaLogo), and also a built-in web server available for local deployment. Using MetaLogo, users can draw informative, customized and publishable sequence logos without any programming experience to present and investigate new knowledge on specific sequence sets.

Investigation of Tribological Behavior and Lubrication Mechanisms of Zinc Oxide under Poly α-olefin Lubrication Enhanced by the Electric Field.

The electric field induces complex effects on the tribological properties of zinc oxide (ZnO) under lubricated conditions, particularly at the nanoscale, where the friction process and mechanism remain unclear. In this paper, the tribological behaviors of ZnO under the lubrication of poly α-olefins (PAO) were investigated by molecular dynamics (MD) simulations with reactive force field (ReaxFF). The results reveal a significant enhancement in the tribological performances of ZnO with the application of the electric field, resulting in a 58.6% reduction in the coefficient of friction (COF) from 0.193 at 0 V/Å to 0.080 at 0.1 V/Å. This improvement can be attributed to the weakening of interfacial interaction, evidenced by a reduction in the number of C-O covalent bonds under the influence of the electric field, along with the formation of an adsorption film due to applied load and shear effects. Notably, the effect of the electric field and applied load extends the impact of interface slip on the tribological performance of ZnO. Overall, this study provides a comprehensive understanding of the impact of the electric field on reducing the friction of ZnO-based structured models, shedding light on explaining their tribological properties and lubrication mechanisms.

Deep learning in computed tomography to predict endotype in chronic rhinosinusitis with nasal polyps.

BACKGROUND: As treatment strategies differ according to endotype, rhinologists must accurately determine the endotype in patients affected by chronic rhinosinusitis with nasal polyps (CRSwNP) for the appropriate management. In this study, we aim to construct a novel deep learning model using paranasal sinus computed tomography (CT) to predict the endotype in patients with CRSwNP. METHODS: We included patients diagnosed with CRSwNP between January 1, 2020, and April 31, 2023. The endotype of patients with CRSwNP in this study was classified as eosinophilic or non-eosinophilic. Sinus CT images (29,993 images) were retrospectively collected, including the axial, coronal, and sagittal planes, and randomly divided into training, validation, and testing sets. A residual network-18 was used to construct the deep learning model based on these images. Loss functions, accuracy functions, confusion matrices, and receiver operating characteristic curves were used to assess the predictive performance of the model. Gradient-weighted class activation mapping was performed to visualize and interpret the operating principles of the model. RESULTS: Among 251 included patients, 86 and 165 had eosinophilic or non-eosinophilic CRSwNP, respectively. The median (interquartile range) patient age was 49 years (37-58 years), and 153 (61.0%) were male. The deep learning model showed good discriminative performance in the training and validation sets, with areas under the curves of 0.993 and 0.966, respectively. To confirm the model generalizability, the receiver operating characteristic curve in the testing set showed good discriminative performance, with an area under the curve of 0.963. The Kappa scores of the confusion matrices in the training, validation, and testing sets were 0.985, 0.928, and 0.922, respectively. Finally, the constructed deep learning model was used to predict the endotype of all patients, resulting in an area under the curve of 0.962. CONCLUSIONS: The deep learning model developed in this study may provide a novel noninvasive method for rhinologists to evaluate endotypes in patients with CRSwNP and help develop precise treatment strategies.

A multi-view fusion lightweight network for CRSwNPs prediction on CT images.

Accurate preoperative differentiation of the chronic rhinosinusitis (CRS) endotype between eosinophilic CRS (eCRS) and non-eosinophilic CRS (non-eCRS) is an important topic in predicting postoperative outcomes and administering personalized treatment. To this end, we have constructed a sinus CT dataset, which comprises CT scan data and pathological biopsy results from 192 patients of chronic rhinosinusitis with nasal polyps (CRSwNP), treated at the Second Affiliated Hospital of Shantou University Medical College between 2020 and 2022. To differentiate CRSwNP endotype on preoperative CT and improve efficiency at the same time, we developed a multi-view fusion model that contains a mini-architecture with each network of 10 layers by modifying the deep residual neural network. The proposed model is trained on a training set and evaluated on a test set. The multi-view deep learning fusion model achieved the area under the receiver-operating characteristics curve (AUC) of 0.991, accuracy of 0.965 and F1-Score of 0.970 in test set. We compared the performance of the mini-architecture with other lightweight networks on the same Sinus CT dataset. The experimental results demonstrate that the developed ResMini architecture contribute to competitive CRSwNP endotype identification modeling in terms of accuracy and parameter number.

Measuring and optimizing the spatial accessibility of primary health care in remote and rural areas: a case study of Liannan Yao Autonomous County in China.

The need for equitable access to primary healthcare services in the current global context has attracted widespread attention, prompting nations to continuously enhance their grassroots medical service levels. In response, China launched the "Healthy China" initiative, which prioritizes the enhancement of national health as a core goal of the healthcare system and uses this opportunity to deepen reforms aimed at strengthening primary care. However, in remote and rural areas, the optimization of medical resource allocation and the achievement of balanced service development remain critical challenges owing to limited resources. This study selected Liannan Yao Autonomous County, which is situated in the northwestern corner of Guangdong Province, as a case study due to its remote mountainous location, underdeveloped economy, and minority region characteristics. Through field research and interviews, this study thoroughly explored the needs of both supply and demand, factoring in elements such as the service capability of healthcare facilities and residents' travel thresholds to enhance the two-step floating catchment area model, thus making it more applicable to remote villages. By integrating electric bikes and cars, which are the primary means of transportation in rural areas, this study conducted a thorough analysis and comparison of the accessibility of medical services in Liannan Yao Autonomous County (Liannan County) . The results reveal significant disparities in healthcare accessibility, an uneven distribution of medical resources, and varying impacts of transportation conditions and facility service capabilities on accessibility. Notably, the study revealed that improving transportation conditions alone has limited effects in rural areas; the key lies in balancing medical service capabilities and the rationality of overall layouts. From the perspectives of equity and efficiency, this study employs the equitable coverage model and the efficiency-driven model to construct two scenarios, comparing accessibility changes in Liannan County under both conditions and proposing strategies to improve the spatial layout of local healthcare facilities. This research not only deepens the understanding of healthcare service accessibility in rural areas but also provides a scientific basis for optimizing resource allocation and enhancing primary medical services, offering valuable guidance and reference for Liannan County and other similar rural regions.

CrnnCrispr: An Interpretable Deep Learning Method for CRISPR/Cas9 sgRNA On-Target Activity Prediction.

CRISPR/Cas9 is a powerful genome-editing tool in biology, but its wide applications are challenged by a lack of knowledge governing single-guide RNA (sgRNA) activity. Several deep-learning-based methods have been developed for the prediction of on-target activity. However, there is still room for improvement. Here, we proposed a hybrid neural network named CrnnCrispr, which integrates a convolutional neural network and a recurrent neural network for on-target activity prediction. We performed unbiased experiments with four mainstream methods on nine public datasets with varying sample sizes. Additionally, we incorporated a transfer learning strategy to boost the prediction power on small-scale datasets. Our results showed that CrnnCrispr outperformed existing methods in terms of accuracy and generalizability. Finally, we applied a visualization approach to investigate the generalizable nucleotide-position-dependent patterns of sgRNAs for on-target activity, which shows potential in terms of model interpretability and further helps in understanding the principles of sgRNA design.

Sensitive photoelectrochemical detection of colitoxin DNA based on NCDs@CuO/ZnO heterostructured nanocomposites with efficient separation capacity of photo-induced carriers.

A metal-organic framework (MOF) of Cu-TPA (terephthalic acid) microsphere was prepared, followed by calcinating the MOF precursor of Cu-TPA/ZIF-8 mixture to obtain the CuO/ZnO. N-doped carbon dots (NCDs) were employed to combine the CuO/ZnO composite to form a tripartite heterostructured architecture of NCDs@CuO/ZnO, which led to a fierce enlargement of the photocurrent response. This  was ascribed to the thinner-shell structure of the CuO microsphere and the fact that hollow ZnO particles could sharply promote the incidence intensity of visible light. The more porous defectiveness exposed on CuO/ZnO surface was in favor of rapidly infiltrating electrolyte ions. The p-n type CuO/ZnO composite with more contact interface could abridge the transfer distance of photo-induced electron (e-1)/hole (h+) pairs and repress their recombination availably. NCDs not only could boost electron transfer rate on the electrode interface but also successfully sensitized the CuO/ZnO composite, which resulted in high conversion efficiency of photon-to-electron. The probe DNA (S1) was firmly assembled on the modified ITO electrode surface (S1/NCDs@CuO/ZnO) through an amidation reaction. Under optimal conditions, the prepared DNA biosensor displayed a wide linear range of 1.0 × 10-6 ~ 7.5 × 10-1 nM and a low limit of detection (LOD) of 1.81 × 10-7 nM for colitoxin DNA (S2) measure, which exhibited a better photoelectrochemistry (PEC) analysis performance than that obtained by differential pulse voltammetry techniques. The relative standard deviation (RSD) of the sensing platform for target DNA detection of 5.0 × 10-2 nM was 6.3%. This proposed DNA biosensor also showed good selectivity, stability, and reproducibility, demonstrating that the well-designed and synthesized photoactive materials of NCDs@CuO/ZnO are promising candidates for PEC analysis.

An enhanced photoelectrochemical biosensor for colitoxin DNA based on HKUST-1/TiO2 and derived HKUST-CuO/TiO2 heterogeneous composites.

HKUST-1 MOFs and its derivative HKUST-CuO were coupled with TiO2 nanoparticles to form the heterogeneous composites of HKUST-1/TiO2 and HKUST-CuO/TiO2 based on their well-suitable bandgap energies (Eg). Compared with mono-component HKUST-1 or HKUST-CuO, the prepared composites displayed photoelectrochemical (PEC) response due to the synergistic effect from their heterogeneous structure. Higher photocurrent response was obtained on HKUST-CuO/TiO2-modified ITO electrode (HKUST-CuO/TiO2/ITO), which could be attributed to the hollow structure with a thin shell of HKUST-CuO greatly enhancing visible spectra harvesting. The CuO component in HKUST-CuO not only could accelerate electron transfer on the heterojunction interface but also effectively separate the photo-generated charge carriers (e-1/h+). Based on the excellent PEC performance of prepared photoactive composite material, under visible-light excitation (λ ≥ 420 nm) and with a working potential of 0 V (vs. Ag/AgCl), the S1 (probe DNA)/HKUST-CuO/TiO2/ITO PEC platform was successfully fabricated for colitoxin DNA detection without using ascorbic acid (AA) as an electron donor. Compared with the analysis results on S1/HKUST-1/TiO2/ITO electrode, S1/HKUST-CuO/TiO2/ITO displayed a wider linear response range from 1.0 × 10-6 to 4.0 × 10-1 nM with a lower detection limit of 3.73 × 10-7 nM (S/N = 3), the linear regression equation was ΔI (10-6 A) =0.5549-0.1858 log (CS2/M), which confirmed the HKUST-CuO could improve sensitivity because of its prominent PEC property. The relative standard deviation (RSD) of the PEC sensor for target DNA detection of 2.0 × 10-4 nM was 7.4%. The proposed DNA biosensor also possessed good specificity and stability. Hence, this reported work was a promising strategy for molecular diagnosis in the bio-analysis field. (A) Schematic illustration of the preparation process of the proposed PEC biosensors for colitoxin DNA detection. (B) The preparation process of HKUST-1 and HKUST-CuO.

A photoelectrochemical aptasensor for thrombin based on the use of carbon quantum dot-sensitized TiO2 and visible-light photoelectrochemical activity.

A photoelectrochemical (PEC) aptasensor for the highly sensitive and specific detection of thrombin is described. This aptasensor is based on an indium tin oxide (ITO) support that is covered with carbon quantum dot (CQD)-sensitized TiO2 and acts as a photoactive matrix. The ITO/TiO2/CQD electrode was prepared by impregnation assembly. It displays an enhanced and steady photocurrent response under irradiation by visible light. A carboxyl-functionalized thrombin-binding aptamer was covalently immobilized on the modified ITO to obtain a PEC aptasensor whose photocurrent decreases with increasing concentration of thrombin. Under 420 nm irradiation at a bias voltage of 0 V, the aptasensor has a linear response in the 1.0 to 250 pM thrombin concentration range, with a 0.83 pM detection limit. Conceivably, this approach can be extended to numerous other PEC aptasensors for the detection of targets for which appropriate aptamers are available. Graphical abstract Schematic of a PEC aptasensor for thrombin. It is based on the use of CQD as the sensitizer, TiO2/CQDs as the photoactive matrix, and the thrombin aptamer as the recognition element.

A novel reverse fluorescent immunoassay approach for sensing human chorionic gonadotropin based on silver-gold nano-alloy and magnetic nanoparticles.

A novel and environmentally friendly reverse fluorescent immunoassay approach was proposed and utilized for sensing human chorionic gonadotropin (HCG) in human serum by coupling a newly prepared and highly fluorescent glutathione-stabilized silver-gold nano-alloy (GSH-AgAuNAs) with magnetic nanoparticles (MNPs). To construct such a reverse system, fluorescent GSH-AgAuNAs and MNPs were first prepared and bio-functionalized with monoclonal antibodies (Mab-I and Mab-II) toward HCG antigen, respectively. Then, the GSH-AgAuNAs functionalized with Mab-I were incubated with HCG, followed by the addition of MNPs attached to Mab-II. Thereafter, a sandwich-type immunoassay could be constructed for determination of HCG owing to the antibody-antigen recognition between the functionalized GSH-AgAuNAs and MNPs. Afterwards, a magnetic collection was employed. Hence, the amount of GSH-AgAuNAs would be reduced through an immuno-magnetic separation, thus weakening the fluorescent intensity. Different from conventional immunoassay, our work determined the quantitative signal by measuring the decreasing gradient fluorescent intensity. Under optimal conditions, the developed reverse method exhibited a wide linear range of 0.5-600 ng mL(-1) toward HCG with a detection limit of 0.25 ng mL(-1). Additionally, the proposed immunoassay was validated using spiked samples, illustrating a satisfactory result in practical application.

An efficient signal-on aptamer-based biosensor for adenosine triphosphate detection using graphene oxide both as an electrochemical and electrochemiluminescence signal indicator.

An efficient aptasensor was developed in which graphene oxide (GO) was employed as an indicator for both electrochemical impedance spectroscopy and electrochemiluminescence (ECL) signal generation. The aptasensor was fabricated by self-assembling the ECL probe of a thiolated adenosine triphosphate binding aptamer (ABA) tagged with a Ru complex (Ru(bpy)3(2+) derivatives) onto the surface of gold nanoparticle (AuNP) modified glassy carbon electrode (GCE). ABA immobilized onto AuNP modified GCE could strongly adsorb GO due to the strong π-π interaction between ABA and graphene oxide; ECL quenching of the Ru complex then takes place because of energy transfer and electron transfer, and a large increase of the electron transfer resistance (Ret) of the electrode. While in the presence of target adenosine triphosphate (ATP), the ABA prefers to form ABA-ATP bioaffinity complexes, which have weak affinity to graphene oxide and keep the graphene oxide away from the electrode surface, thus allowing the ECL signal enhancement, and in conjunction with the decrease of the Ret. Because of the high ECL quenching efficiency, unique structure, and electronic properties of graphene oxide, the Ret and ECL intensity versus the logarithm of ATP concentration was linear in the wide range from 10 pM to 10 nM with an ultra-low detection limit of 6.7 pM to 4.8 pM, respectively. The proposed aptasensor exhibited excellent reproducibility, stability, and outstanding selectivity, and ATP could be effectively distinguished from its analogues. More significantly, this efficient ECL aptasensor strategy based on GO acting both as an electrochemical and ECL signal indicator is general and can be easily extended to other biological binding events.

Hair mercury concentrations and associated factors in an electronic waste recycling area, Guiyu, China.

OBJECTIVE: Toxic heavy metals are released to the environment constantly from unregulated electronic waste (e-waste) recycling in Guiyu, China, and thus may contribute to the elevation of mercury (Hg) and other heavy metals levels in human hair. We aimed to investigate concentrations of mercury in hair from Guiyu and potential risk factors and compared them with those from a control area where no e-waste processing occurs. METHODS: A total of 285 human hair samples were collected from three villages (including Beilin, Xianma, and Huamei) of Guiyu (n=205) and the control area, Jinping district of Shantou city (n=80). All the volunteers were administered a questionnaire regarding socio-demographic characteristics and other possible factors contributed to hair mercury concentration. Hair mercury concentration was analyzed by hydride generation atomic fluorescence spectrometry (AFS). RESULTS: Our results suggested that hair mercury concentrations in volunteers of Guiyu (median, 0.99; range, 0.18-3.98µg/g) were significantly higher than those of Jinping (median, 0.59; range, 0.12-1.63µg/g). We also observed a higher over-limit ratio (>1µg/g according to USEPA) in Guiyu than in Jinping (48.29% vs. 11.25%, P<0.001). Logistic regression model showed that the variables of living house also served as an e-waste workshop, work related to e-waste, family income, time of residence in Guiyu, the distance between home and waste incineration, and fish intake were associated with hair mercury concentration. After multiple stepwise regression analysis, in the Guiyu samples, hair mercury concentration was found positively associated with the time residence in Guiyu (ß=0.299, P<0.001), and frequency of shellfish intake (ß=0.184, P=0.016); and negatively associated with the distance between home and waste incineration (ß=-0.190, P=0.015) and whether house also served as e-waste workshop (ß=-0.278, P=0.001). CONCLUSIONS: This study investigated human mercury exposure and suggested elevated hair mercury concentrations in an e-waste recycling area, Guiyu, China. Living in Guiyu for a long time and work related to e-waste may primarily contribute to the high hair mercury concentrations.

Ultrasonic oscillation dialysis-graphite furnace atomic absorption spectrometer method for determination of "free" copper and exchangeable copper in serum.

BACKGROUND: The determination of copper (Cu) in human blood is important in medical diagnosis. However, its biological activities strongly depend on the chemical forms, and thus data for total Cu concentration is not sufficient for medical diagnosis or mechanism study. Therefore, analyses of copper species in serum have much more physiologically meaningful. METHODS: Ultrasonic oscillation dialysis procedure was introduced in the determination of "free" Cu, more precisely PBS dialysis Cu (DiaCu), and exchangeable Cu (EXCu) by graphite furnace atomic absorption spectrometry (GFAAS). Then the levels of serum "free" Cu and EXCu from hepatitis, liver cancer, and cervical cancer patients were determined. RESULTS: The accuracy of the method ranged from 92% - 97% for PBS DiaCu and 90% - 107% for EXCu when using a dialysis membrane pore size of 25 kDa. The regression equation of the calibration curve was expressed as y = bChi + a, with linear regression coefficients (r2) of 0.9999 for ultrapure water and 0.9998 for PBS buffer and EDTA buffer. The limit of detection is 0.76 microg/L. Application of this method to serum samples showed that the levels of"free" Cu and EXCu in serum changed in various pathophysiological conditions. CONCLUSIONS: Ultrasonic oscillation dialysis-GFAAS method described here for the speciation of "free" Cu and EXCu in serum is simple with good reproducibility and small sample volume.

Interpretable CRISPR/Cas9 off-target activities with mismatches and indels prediction using BERT.

Off-target effects of CRISPR/Cas9 can lead to suboptimal genome editing outcomes. Numerous deep learning-based approaches have achieved excellent performance for off-target prediction; however, few can predict the off-target activities with both mismatches and indels between single guide RNA (sgRNA) and target DNA sequence pair. In addition, data imbalance is a common pitfall for off-target prediction. Moreover, due to the complexity of genomic contexts, generating an interpretable model also remains challenged. To address these issues, firstly we developed a BERT-based model called CRISPR-BERT for enhancing the prediction of off-target activities with both mismatches and indels. Secondly, we proposed an adaptive batch-wise class balancing strategy to combat the noise exists in imbalanced off-target data. Finally, we applied a visualization approach for investigating the generalizable nucleotide position-dependent patterns of sgRNA-DNA pair for off-target activity. In our comprehensive comparison to existing methods on five mismatches-only datasets and two mismatches-and-indels datasets, CRISPR-BERT achieved the best performance in terms of AUROC and PRAUC. Besides, the visualization analysis demonstrated how implicit knowledge learned by CRISPR-BERT facilitates off-target prediction, which shows potential in model interpretability. Collectively, CRISPR-BERT provides an accurate and interpretable framework for off-target prediction, further contributes to sgRNA optimization in practical use for improved target specificity in CRISPR/Cas9 genome editing. The source code is available at https://github.com/BrokenStringx/CRISPR-BERT.

An End-to-End CRSwNP Prediction with Multichannel ResNet on Computed Tomography.

Chronic rhinosinusitis (CRS) is a global disease characterized by poor treatment outcomes and high recurrence rates, significantly affecting patients' quality of life. Due to its complex pathophysiology and diverse clinical presentations, CRS is categorized into various subtypes to facilitate more precise diagnosis, treatment, and prognosis prediction. Among these, CRS with nasal polyps (CRSwNP) is further divided into eosinophilic CRSwNP (eCRSwNP) and noneosinophilic CRSwNP (non-eCRSwNP). However, there is a lack of precise predictive diagnostic and treatment methods, making research into accurate diagnostic techniques for CRSwNP endotypes crucial for achieving precision medicine in CRSwNP. This paper proposes a method using multiangle sinus computed tomography (CT) images combined with artificial intelligence (AI) to predict CRSwNP endotypes, distinguishing between patients with eCRSwNP and non-eCRSwNP. The considered dataset comprises 22,265 CT images from 192 CRSwNP patients, including 13,203 images from non-eCRSwNP patients and 9,062 images from eCRSwNP patients. Test results from the network model demonstrate that multiangle images provide more useful information for the network, achieving an accuracy of 98.43%, precision of 98.1%, recall of 98.1%, specificity of 98.7%, and an AUC value of 0.984. Compared to the limited learning capacity of single-channel neural networks, our proposed multichannel feature adaptive fusion model captures multiscale spatial features, enhancing the model's focus on crucial sinus information within the CT images to maximize detection accuracy. This deep learning-based diagnostic model for CRSwNP endotypes offers excellent classification performance, providing a noninvasive method for accurately predicting CRSwNP endotypes before treatment and paving the way for precision medicine in the new era of CRSwNP.

Quantitative separation of CEST effect by Rex-line-fit analysis of Z-spectra.

The process of chemical exchange saturation transfer (CEST) is quantified by evaluating a Z-spectra, where CEST signal quantification and Z-spectra fitting have been widely used to distinguish the contributions from multiple origins. Based on the exchange-dependent relaxation rate in the rotating frame (Rex), this paper introduces an additional pathway to quantitative separation of CEST effect. The proposed Rex-line-fit method is solved by a multi-pool model and presents the advantage of only being dependent of the specific parameters (solute concentration, solute-water exchange rate, solute transverse relaxation, and irradiation power). Herein we show that both solute-water exchange rate and solute concentration monotonously vary with Rex for Amide, Guanidino, NOE and MT, which has the potential to assist in solving quantitative separation of CEST effect. Furthermore, we achieve Rex imaging of Amide, Guanidino, NOE and MT, which may provide direct insight into the dependency of measurable CEST effects on underlying parameters such as the exchange rate and solute concentration, as well as the solute transverse relaxation.

Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS.

Integrating single-cell datasets produced by multiple omics technologies is essential for defining cellular heterogeneity. Mosaic integration, in which different datasets share only some of the measured modalities, poses major challenges, particularly regarding modality alignment and batch effect removal. Here, we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation and batch correction of mosaic data by using self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to 19 other methods and reliability by evaluating its performance in trimodal and mosaic integration tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS. MIDAS is available at https://github.com/labomics/midas .

A ResNet mini architecture for brain age prediction.

The brain presents age-related structural and functional changes in the human life, with different extends between subjects and groups. Brain age prediction can be used to evaluate the development and aging of human brain, as well as providing valuable information for neurodevelopment and disease diagnosis. Many contributions have been made for this purpose, resorting to different machine learning methods. To solve this task and reduce memory resource consumption, we develop a mini architecture of only 10 layers by modifying the deep residual neural network (ResNet), named ResNet mini architecture. To support the ResNet mini architecture in brain age prediction, the brain age dataset (OpenNeuro #ds000228) that consists of 155 study participants (three classes) and the Alzheimer MRI preprocessed dataset that consists of 6400 images (four classes) are employed. We compared the performance of the ResNet mini architecture with other popular networks using the two considered datasets. Experimental results show that the proposed architecture exhibits generality and robustness with high accuracy and less parameter number.

A sensitive electrochemiluminescence immunosensor for the detection of CA15-3 based on CeO2/Pt/rGO as a novel co-reaction accelerator.

In this work, we successfully constructed a label-free electrochemiluminescence (ECL) immunosensor for the detection of breast cancer marker antigen (CA15-3). In particular, 3,4,9,10-perylenetetracarboxylic acid (PTCA) is cleverly attached to the surface of silica spheres as a luminophore (NH2-SiO2-PTCA), which greatly alleviates the disadvantage of PTCA anti-induced aggregated luminescence and improves the ECL performance. Furthermore, Pt nanoparticles were used to dope CeO2 and introducing reduced graphene oxide (rGO) to prepare CeO2/Pt/rGO composites as a novel co-reaction accelerator. Among them, Pt nanoparticles were used to improve the electrical conductivity of CeO2, and the use of rGO as a substrate allows for a more uniform dispersion of CeO2 to increase the catalytic surface area, which effectively improves the performance of the co-reaction accelerator and thus increasing the ECL intensity of the PTCA/S2O82- system. Under the optimal conditions, the designed ECL immunosensor showed satisfactory results in the determination of CA15-3 with a linear range of 12.00 mU mL-1 - 120.00 U mL-1 and a low detection limit of 1.348 mU mL-1. Importantly, the resulting biosensor has good stability, high sensitivity and reliable reproducibility, suggesting its potential application in clinical research.