Combining EEG Features and Convolutional Autoencoder for Neonatal Seizure Detection.

Neonatal epilepsy is a common emergency phenomenon in neonatal intensive care units (NICUs), which requires timely attention, early identification, and treatment. Traditional detection methods mostly use supervised learning with enormous labeled data. Hence, this study offers a semi-supervised hybrid architecture for detecting seizures, which combines the extracted electroencephalogram (EEG) feature dataset and convolutional autoencoder, called Fd-CAE. First, various features in the time domain and entropy domain are extracted to characterize the EEG signal, which helps distinguish epileptic seizures subsequently. Then, the unlabeled EEG features are fed into the convolutional autoencoder (CAE) for training, which effectively represents EEG features by optimizing the loss between the input and output features. This unsupervised feature learning process can better combine and optimize EEG features from unlabeled data. After that, the pre-trained encoder part of the model is used for further feature learning of labeled data to obtain its low-dimensional feature representation and achieve classification. This model is performed on the neonatal EEG dataset collected at the University of Helsinki Hospital, which has a high discriminative ability to detect seizures, with an accuracy of 92.34%, precision of 93.61%, recall rate of 98.74%, and F1-score of 95.77%, respectively. The results show that unsupervised learning by CAE is beneficial to the characterization of EEG signals, and the proposed Fd-CAE method significantly improves classification performance.

A Fusion Learning Model Based on Deep Learning for Single-Cell RNA Sequencing Data Clustering.

Single-cell RNA sequencing (scRNA-seq) technology provides a means for studying biology from a cellular perspective. The fundamental goal of scRNA-seq data analysis is to discriminate single-cell types using unsupervised clustering. Few single-cell clustering algorithms have taken into account both deep and surface information, despite the recent slew of suggestions. Consequently, this article constructs a fusion learning framework based on deep learning, namely scGASI. For learning a clustering similarity matrix, scGASI integrates data affinity recovery and deep feature embedding in a unified scheme based on various top feature sets. Next, scGASI learns the low-dimensional latent representation underlying the data using a graph autoencoder to mine the hidden information residing in the data. To efficiently merge the surface information from raw area and the deeper potential information from underlying area, we then construct a fusion learning model based on self-expression. scGASI uses this fusion learning model to learn the similarity matrix of an individual feature set as well as the clustering similarity matrix of all feature sets. Lastly, gene marker identification, visualization, and clustering are accomplished using the clustering similarity matrix. Extensive verification on actual data sets demonstrates that scGASI outperforms many widely used clustering techniques in terms of clustering accuracy.

Palladium-Catalyzed Regioselective B(3,5)-Dialkenylation and B(4)-Alkenylation of o-Carboranes.

Picolyl group directed B(3,5)-dialkenylation and B(4)-monoalkenylation of o-carboranes has been developed with a very low palladium catalyst loading. The degree of substitution is determined by the cage C(2)-substituents due to steric reasons. On the basis of experimental results, a plausible mechanism is proposed including electrophilic palladation and alkyne insertion followed by protonation.

A Clustering Method for Single-Cell RNA-Seq Data Based on Automatic Weighting Penalty and Low-Rank Representation.

Advances in high-throughput single-cell RNA sequencing (scRNA-seq) technology have provided more comprehensive biological information on cell expression. Clustering analysis is a critical step in scRNA-seq research and provides clear knowledge of the cell identity. Unfortunately, the characteristics of scRNA-seq data and the limitations of existing technologies make clustering encounter a considerable challenge. Meanwhile, some existing methods treat different features equally and ignore differences in feature contributions, which leads to a loss of information. To overcome limitations, we introduce a weighted distance constraint into the construction of the similarity graph and combine the similarity constraint. We propose the Joint Automatic Weighting Similarity Graph and Low-rank Representation (JAGLRR) clustering method. Evaluating the contributions of each feature and assigning various weight values can increase the significance of valuable features while decreasing the interference of redundant features. The similarity constraint allows the model to generate a more symmetric affinity matrix. Benefitting from that affinity matrix, JAGLRR recovers the original linear relationship of the data more accurately and obtains more discriminative information. The results on simulated datasets and 8 real datasets show that JAGLRR outperforms 11 existing comparison methods in clustering experiments, with higher clustering accuracy and stability.

Epileptic Seizure Detection with an End-to-End Temporal Convolutional Network and Bidirectional Long Short-Term Memory Model.

Automatic seizure detection plays a key role in assisting clinicians for rapid diagnosis and treatment of epilepsy. In view of the parallelism of temporal convolutional network (TCN) and the capability of bidirectional long short-term memory (BiLSTM) in mining the long-range dependency of multi-channel time-series, we propose an automatic seizure detection method with a novel end-to-end TCN-BiLSTM model in this work. First, raw EEG is filtered with a 0.5-45 Hz band-pass filter, and the filtered data are input into the proposed TCN-BiLSTM network for feature extraction and classification. Post-processing process including moving average filtering, thresholding and collar technique is then employed to further improve the detection performance. The method was evaluated on two EEG database. On the CHB-MIT scalp EEG database, our method achieved a segment-based sensitivity of 94.31%, specificity of 97.13%, and accuracy of 97.09%. Meanwhile, an event-based sensitivity of 96.48% and an average false detection rate (FDR) of 0.38/h were obtained. On the SH-SDU database we collected, the segment-based sensitivity of 94.99%, specificity of 93.25%, and accuracy of 93.27% were achieved. In addition, an event-based sensitivity of 99.35% and a false detection rate of 0.54/h were yielded. The total detection time consumed for 1[Formula: see text]h EEG data was 5.65[Formula: see text]s. These results demonstrate the superiority and promising potential of the proposed method in real-time monitoring of epileptic seizures.

Joint L2,p-norm and random walk graph constrained PCA for single-cell RNA-seq data.

The development and widespread utilization of high-throughput sequencing technologies in biology has fueled the rapid growth of single-cell RNA sequencing (scRNA-seq) data over the past decade. The development of scRNA-seq technology has significantly expanded researchers' understanding of cellular heterogeneity. Accurate cell type identification is the prerequisite for any research on heterogeneous cell populations. However, due to the high noise and high dimensionality of scRNA-seq data, improving the effectiveness of cell type identification remains a challenge. As an effective dimensionality reduction method, Principal Component Analysis (PCA) is an essential tool for visualizing high-dimensional scRNA-seq data and identifying cell subpopulations. However, traditional PCA has some defects when used in mining the nonlinear manifold structure of the data and usually suffers from over-density of principal components (PCs). Therefore, we present a novel method in this paper called joint L2,p-norm and random walk graph constrained PCA (RWPPCA). RWPPCA aims to retain the data's local information in the process of mapping high-dimensional data to low-dimensional space, to more accurately obtain sparse principal components and to then identify cell types more precisely. Specifically, RWPPCA combines the random walk (RW) algorithm with graph regularization to more accurately determine the local geometric relationships between data points. Moreover, to mitigate the adverse effects of dense PCs, the L2,p-norm is introduced to make the PCs sparser, thus increasing their interpretability. Then, we evaluate the effectiveness of RWPPCA on simulated data and scRNA-seq data. The results show that RWPPCA performs well in cell type identification and outperforms other comparison methods.

Effects of vertical integration on the healthcare system in China: a systematic review and meta-analysis.

Vertical integration is one possible way to improve the performance of a healthcare system; however, its effects are inconsistent, and there is a lack of evidence from undeveloped nations. This study aims to systematically review the evidence regarding effects of vertical integration on healthcare systems in China. We searched PubMed, Embase, Cochrane Library, Web of Science, ProQuest Health & Medicine Collection, China Knowledge Resource Integrated Database and Wanfang databases from April 2009 (initiation of new healthcare reform) to May 2021 for randomized controlled trials (RCTs), controlled before and after (CBA) trials, cohort studies and interrupted time series (ITS) trials. Vertical integration in the included studies must involve both primary health institutions and secondary or tertiary hospitals. After screening 3109 records, we ultimately analysed 47 studies, including 27 CBA trials, 18 RCTs and 2 ITS trials. The narrative synthesis shows that all but three studies indicated that vertical integration improved efficiency (utilization and cost of health services), quality of public health services and medical services, health provider-centred outcomes (knowledge and skill) and patient-centred outcomes (patients' clinical outcomes, behaviour and satisfaction). Despite the heterogeneity of vertical integration interventions across different studies, the meta-analysis reveals that it lowered diastolic blood pressure (mean difference (MD) -8.41, 95% confidence interval (CI) -15.18 to -1.65) and systolic blood pressure (MD-5.83, 95% CI -9.25 to -2.40) among hypertension patients, and it lowered HbA1c levels (MD -1.95, 95% CI -2.69 to -1.21), fasting blood glucose levels (MD -1.02, 95% CI -1.53 to -0.50) and 2-hour postprandial blood glucose levels (MD -1.78, 95% CI -2.67 to -0.89). The treatment compliance behaviour was improved for hypertension participants (risk ratio (RR) 1.08, 95% CI 1.04-1.13) and for diabetes patients (RR 1.32, 95% CI 1.08-1.61). Vertical integration in China can improve efficiency, quality of care, health provider-centred outcomes and patient-centred outcomes, but high-quality original studies are highly needed.

CCR2 is a potential therapeutic target in peri-implantitis.

AIM: CCR2 (C-C chemokine receptor type 2) plays a crucial role in inflammatory and bone metabolic diseases; however, its role in peri-implantitis remains unclear. This study aimed to explore whether CCR2 contributes to peri-implantitis and the treatment effects of cenicriviroc (CVC) on peri-implant inflammation and bone resorption. MATERIALS AND METHODS: The expression of CCR2 was studied using clinical tissue analysis and an in vivo peri-implantitis model. The role of CCR2 in promoting inflammation and bone resorption in peri-implantitis was evaluated in Ccr2-/- mice and wild-type mice. The effect of CVC on peri-implantitis was evaluated using systemic and local dosage forms. RESULTS: Human peri-implantitis tissues showed increased CCR2 and CCL2 levels, which were positively correlated with bone loss around the implants. Knocking out Ccr2 in an experimental model of peri-implantitis resulted in decreased monocyte and macrophage infiltration, reduced pro-inflammatory cytokine generation and impaired osteoclast activity, leading to reduced inflammation and bone loss around the implants. Treatment with CVC ameliorated bone loss in experimental peri-implantitis. CONCLUSIONS: CCR2 may be a potential target for peri-implantitis treatment by harnessing the immune-inflammatory response to modulate the local inflammation and osteoclast activity.

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma.

BACKGROUND: The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. PURPOSE: This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. STUDY TYPE: Retrospective. POPULATION: 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). FIELD STRENGTH/SEQUENCE: 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). ASSESSMENT: Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. STATISTICAL TESTS: Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. RESULTS: The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). DATA CONCLUSION: MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Vessels encapsulating tumor clusters: a novel efficacy predictor of hepatic arterial infusion chemotherapy in unresectable hepatocellular carcinoma.

PURPOSE: Vessels encapsulating tumor clusters (VETC) is a novel vascular pattern structurally and functionally distinct from microvascular invasion (MVI) in hepatocellular carcinoma (HCC). This study aims to explore the prognostic value of VETC in patients receiving hepatic arterial infusion chemotherapy (HAIC) for unresectable HCC. METHODS: From January 2016 to December 2017, 145 patients receiving HAIC as the initial treatment for unresectable HCC were enrolled and stratified into two groups according to their VETC status. Overall survival (OS), progression-free survival (PFS), overall response rate (ORR), and disease control rate (DCR) were evaluated. RESULTS: The patients were divided into two groups: VETC+ (n = 31, 21.8%) and VETC- (n = 114, 78.2%). The patients in the VETC+ group had worse ORR and DCR than those in the VETC- group (RECIST: ORR: 25.8% vs. 47.4%, P = 0.031; DCR: 56.1% vs. 76.3%, P = 0.007; mRECIST: ORR: 41.0% vs. 52.6%, P = 0.008; DCR: 56.1% vs. 76.3%, P = 0.007). Patients with VETC+ had significantly shorter OS and PFS than those with VETC- (median OS: 10.2 vs. 21.6 months, P < 0.001; median PFS: 3.3 vs. 7.2 months, P < 0.001). Multivariate analysis revealed VETC status as an independent prognostic factor for OS (HR: 2.40; 95% CI: 1.46-3.94; P = 0.001) and PFS (HR: 1.97; 95% CI: 1.20-3.22; P = 0.007). CONCLUSION: VETC status correlates remarkably well with the tumor response and long-term survival in patients undergoing HAIC. It may be a promising efficacy predictor and help identify patients who will benefit from HAIC.

Combination of Sequencing Batch Operation and A/O Process to Achieve Partial Mainstream Anammox: Pilot-Scale Demonstration and Microbial Ecological Mechanism.

In this study, sequencing batch operation was successfully combined with a pilot-scale anaerobic biofilm-modified anaerobic/aerobic membrane bioreactor to achieve anaerobic ammonium oxidation (anammox) without inoculation of anammox aggregates for municipal wastewater treatment. Both total nitrogen and phosphorus removal efficiencies of the reactor reached up to 80% in the 250-day operation, with effluent concentrations of 4.95 mg-N/L and 0.48 mg-P/L. In situ enrichment of anammox bacteria with a maximum relative abundance of 7.86% was observed in the anaerobic biofilm, contributing to 18.81% of nitrogen removal, with denitrification being the primary removal pathway (38.41%). Denitrifying phosphorus removal (DPR) (40.54%) and aerobic phosphorus uptake (48.40%) played comparable roles in phosphorus removal. Metagenomic sequencing results showed that the biofilm contained significantly lower abundances of NO-reducing functional genes than the bulk sludge (p < 0.01), favoring anammox catabolism in the former. Interactions between the anammox bacteria and flanking community were dominated by cooperation behaviors (e.g., nitrite supply, amino acids/vitamins exchange) in the anaerobic biofilm community network. Moreover, the hydrolytic/fermentative bacteria and endogenous heterotrophic bacteria (Dechloromonas, Candidatus competibacter) were substantially enriched under sequencing batch operation, which could alleviate the inhibition of anammox bacteria by complex organics. Overall, this study provides a feasible and promising strategy for substantially enriching anammox bacteria and achieving partial mainstream anammox as well as DPR.

CCL2 is a key regulator and therapeutic target for periodontitis.

AIM: Our previous study revealed that the C-C motif chemokine receptor 2 (CCR2) is a promising target for periodontitis prevention and treatment. However, CCR2 is a receptor with multiple C-C motif chemokine ligands (CCLs), including CCL2, CCL7, CCL8, CCL13 and CCL16, and which of these ligands plays a key role in periodontitis remains unclear. The aim of the present study was to explore the key functional ligand of CCR2 in periodontitis and to evaluate the potential of the functional ligand as a therapeutic target for periodontitis. MATERIALS AND METHODS: The expression levels and clinical relevance of CCR2, CCL2, CCL7, CCL8, CCL13 and CCL16 were studied using human samples. The role of CCL2 in periodontitis was evaluated by using CCL2 knockout mice and overexpressing CCL2 in the periodontium. The effect of local administration of bindarit in periodontitis was evaluated by preventive and therapeutic medication in a mouse periodontitis model. Microcomputed tomography, haematoxylin and eosin staining, tartrate-resistant acid phosphatase staining, real-time quantitative polymerase chain reaction, enzyme-linked immunosorbent assay, bead-based immunoassays and flow cytometry were used for histomorphology, molecular biology and cytology analysis. RESULTS: Among different ligands of CCR2, only CCL2 was significantly up-regulated in periodontitis gingival tissues and was positively correlated with the severity of periodontitis. Mice lacking CCL2 showed milder inflammation and less bone resorption than wild-type mice, which was accompanied by a reduction in monocyte/macrophage recruitment. Adeno-associated virus-2 vectors overexpressing CCL2 in Ccl2-/- mice gingiva reversed the attenuation of periodontitis in a CCR2-dependent manner. In ligation-induced experimental periodontitis, preventive or therapeutic administration of bindarit, a CCL2 synthesis inhibitor, significantly inhibited the production of CCL2, decreased the osteoclast number and bone loss and reduced the expression levels of proinflammatory cytokines TNF-α, IL-6 and IL-1ß. CONCLUSIONS: CCL2 is a pivotal chemokine that binds to CCR2 during the progression of periodontitis, and targeting CCL2 may be a feasible option for controlling periodontitis.

Ecological mechanisms of biofilm development in the hybrid sludge-biofilm process: Implications for process start-up and optimization.

The hybrid sludge-biofilm processes have been widely applied for the construction or upgradation of biological wastewater treatment process. Ecological mechanisms of biofilm development remain unclear in the hybrid ecosystem, because of the intricate interactive effects between sludge and biofilms. Herein, the establishment principles of biofilms with distinct coexisting sludge amounts were uncovered by varying sludge retention times (SRTs) from 5 to 40 days in the hybrid process. With the increasing of SRTs, biofilm biomass decreased with the increase of suspended sludge, resulting in lower biofilm proportion. As estimated by the Gompertz growth model, the increased sludge amounts (i.e., higher SRTs of 20 and 40 days) prolonged the initial colonization stage and decreased the specific development rate of biofilms when compared to lower sludge amounts with the shorter SRTs (i.e., 5 and 10 days). Null model analysis demonstrated that deterministic homogenous selection could facilitate the colonization and accumulation of biofilms with less coexisting sludge (SRT of 10 days). However, stochastic ecological drift and homogenizing dispersal dominated the colonization and accumulation stages of biofilms with more coexisting sludge (SRT of 20 days), respectively. The ecological networks reflected that positively-related taxa presented taxonomic relatedness, whereas high inconsistency of taxonomic relatedness was observed among aggregate forms or development stages as affected by varied SRTs. The high incidence of intra-taxa co-occurrence patterns suggested that taxa with similar ecological niches could be specifically selected in biofilms when being exposed with less coexisting sludge. This study uncovered ecological mechanisms of biofilm development driven by varying the SRTs of suspended sludge, which would help to propose appropriate strategies for the efficient start-up and optimization of the hybrid sludge-biofilm system.

Multi-View Enhanced Tensor Nuclear Norm and Local Constraint Model for Cancer Clustering and Feature Gene Selection.

The analysis of cancer data from multi-omics can effectively promote cancer research. The main focus of this article is to cluster cancer samples and identify feature genes to reveal the correlation between cancers and genes, with the primary approach being the analysis of multi-view cancer omics data. Our proposed solution, the Multi-View Enhanced Tensor Nuclear Norm and Local Constraint (MVET-LC) model, aims to utilize the consistency and complementarity of omics data to support biological research. The model is designed to maximize the utilization of multi-view data and incorporates a nuclear norm and local constraint to achieve this goal. The first step involves introducing the concept of enhanced partial sum of tensor nuclear norm, which significantly enhances the flexibility of the tensor nuclear norm. After that, we incorporate total variation regularization into the MVET-LC model to further augment its performance. It enables MVET-LC to make use of the relationship between tensor data structures and sparse data while paying attention to the feature details of the tensor data. To tackle the iterative optimization problem of MVET-LC, the alternating direction method of multipliers is utilized. Through experimental validation, it is demonstrated that our proposed model outperforms other comparison models.

NLRRC: A Novel Clustering Method of Jointing Non-Negative LRR and Random Walk Graph Regularized NMF for Single-Cell Type Identification.

The development of single-cell RNA sequencing (scRNA-seq) technology has opened up a new perspective for us to study disease mechanisms at the single cell level. Cell clustering reveals the natural grouping of cells, which is a vital step in scRNA-seq data analysis. However, the high noise and dropout of single-cell data pose numerous challenges to cell clustering. In this study, we propose a novel matrix factorization method named NLRRC for single-cell type identification. NLRRC joins non-negative low-rank representation (LRR) and random walk graph regularized NMF (RWNMFC) to accurately reveal the natural grouping of cells. Specifically, we find the lowest rank representation of single-cell samples by non-negative LRR to reduce the difficulty of analyzing high-dimensional samples and capture the global information of the samples. Meanwhile, by using random walk graph regularization (RWGR) and NMF, RWNMFC captures manifold structure and cluster information before generating a cluster allocation matrix. The cluster assignment matrix contains cluster labels, which can be used directly to get the clustering results. The performance of NLRRC is validated on simulated and real single-cell datasets. The results of the experiments illustrate that NLRRC has a significant advantage in single-cell type identification.

BioSTD: A New Tensor Multi-View Framework via Combining Tensor Decomposition and Strong Complementarity Constraint for Analyzing Cancer Omics Data.

Advances in omics technology have enriched the understanding of the biological mechanisms of diseases, which has provided a new approach for cancer research. Multi-omics data contain different levels of cancer information, and comprehensive analysis of them has attracted wide attention. However, limited by the dimensionality of matrix models, traditional methods cannot fully use the key high-dimensional global structure of multi-omics data. Moreover, besides global information, local features within each omics are also critical. It is necessary to consider the potential local information together with the high-dimensional global information, ensuring that the shared and complementary features of the omics data are comprehensively observed. In view of the above, this article proposes a new tensor integrative framework called the strong complementarity tensor decomposition model (BioSTD) for cancer multi-omics data. It is used to identify cancer subtype specific genes and cluster subtype samples. Different from the matrix framework, BioSTD utilizes multi-view tensors to coordinate each omics to maximize high-dimensional spatial relationships, which jointly considers the different characteristics of different omics data. Meanwhile, we propose the concept of strong complementarity constraint applicable to omics data and introduce it into BioSTD. Strong complementarity is used to explore the potential local information, which can enhance the separability of different subtypes, allowing consistency and complementarity in the omics data to be fully represented. Experimental results on real cancer datasets show that our model outperforms other advanced models, which confirms its validity.

Precise microdissection of gastric mixed adeno-neuroendocrine carcinoma dissects its genomic landscape and evolutionary clonal origins.

Gastric mixed adenoneuroendocrine carcinoma (MANEC) is a clinically aggressive and heterogeneous tumor composed of adenocarcinoma (ACA) and neuroendocrine carcinoma (NEC). The genomic properties and evolutionary clonal origins of MANEC remain unclear. We conduct whole-exome and multiregional sequencing on 101 samples from 33 patients to elucidate their evolutionary paths. We identify four significantly mutated genes, TP53, RB1, APC, and CTNNB1. MANEC resembles chromosomal instability stomach adenocarcinoma in that whole-genome doubling in MANEC is predominant and occurs earlier than most copy-number losses. All tumors are of monoclonal origin, and NEC components show more aggressive genomic properties than their ACA counterparts. The phylogenetic trees show two tumor divergence patterns, including sequential and parallel divergence. Furthermore, ACA-to-NEC rather than NEC-to-ACA transition is confirmed by immunohistochemistry on 6 biomarkers in ACA- and NEC-dominant regions. These results provide insights into the clonal origin and tumor differentiation of MANEC.

Treatment cost assessment for COVID-19 inpatients in Shenzhen, China 2020-2021: facts and suggestions.

Background: Knowledge regarding the treatment cost of coronavirus disease 2019 (COVID-19) in the real world is vital for disease burden forecasts and health resources planning. However, it is greatly hindered by obtaining reliable cost data from actual patients. To address this knowledge gap, this study aims to estimate the treatment cost and specific cost components for COVID-19 inpatients in Shenzhen city, China in 2020-2021. Methods: It is a 2 years' cross-sectional study. The de-identified discharge claims were collected from the hospital information system (HIS) of COVID-19 designated hospital in Shenzhen, China. One thousand three hundred ninety-eight inpatients with a discharge diagnosis for COVID-19 from January 10, 2020 (the first COVID-19 case admitted in the hospital in Shenzhen) to December 31, 2021. A comparison was made of treatment cost and cost components of COVID-19 inpatients among seven COVID-19 clinical classifications (asymptomatic, mild, moderate, severe, critical, convalescent and re-positive cases) and three admission stages (divided by the implementation of different treatment guidelines). The multi-variable linear regression models were used to conduct the analysis. Results: The treatment cost for included COVID-19 inpatients was USD 3,328.8. The number of convalescent cases accounted for the largest proportion of all COVID-19 inpatients (42.7%). The severe and critical cases incurred more than 40% of treatment cost on western medicine, while the other five COVID-19 clinical classifications spent the largest proportion (32%-51%) on lab testing. Compared with asymptomatic cases, significant increases of treatment cost were observed in mild cases (by 30.0%), moderate cases (by 49.2%), severe cases (by 228.7%) and critical cases (by 680.7%), while reductions were shown in re-positive cases (by 43.1%) and convalescent cases (by 38.6%). The decreasing trend of treatment cost was observed during the latter two stages by 7.6 and 17.9%, respectively. Conclusions: Our findings identified the difference of inpatient treatment cost across seven COVID-19 clinical classifications and the changes at three admission stages. It is highly suggestive to inform the financial burden experienced by the health insurance fund and the Government, to emphasize the rational use of lab tests and western medicine in the COVID-19 treatment guideline, and to design suitable treatment and control policy for convalescent cases.

A Personalized Low-Rank Subspace Clustering Method Based on Locality and Similarity Constraints for scRNA-seq Data Analysis.

Single-cell RNA sequencing (scRNA-seq) technology can provide expression profile of single cells, which propels biological research into a new chapter. Clustering individual cells based on their transcriptome is a critical objective of scRNA-seq data analysis. However, the high-dimensional, sparse and noisy nature of scRNA-seq data pose a challenge to single-cell clustering. Therefore, it is urgent to develop a clustering method targeting scRNA-seq data characteristics. Due to its powerful subspace learning capability and robustness to noise, the subspace segmentation method based on low-rank representation (LRR) is broadly used in clustering researches and achieves satisfactory results. In view of this, we propose a personalized low-rank subspace clustering method, namely PLRLS, to learn more accurate subspace structures from both global and local perspectives. Specifically, we first introduce the local structure constraint to capture the local structure information of the data, while helping our method to obtain better inter-cluster separability and intra-cluster compactness. Then, in order to retain the important similarity information that is ignored by the LRR model, we utilize the fractional function to extract similarity information between cells, and introduce this information as the similarity constraint into the LRR framework. The fractional function is an efficient similarity measure designed for scRNA-seq data, which has theoretical and practical implications. In the end, based on the LRR matrix learned from PLRLS, we perform downstream analyses on real scRNA-seq datasets, including spectral clustering, visualization and marker gene identification. Comparative experiments show that the proposed method achieves superior clustering accuracy and robustness.

Deciphering community assembly and succession in sequencing batch moving bed biofilm reactor: Differentiation between attached and suspended communities.

Elucidating community assembly and succession is crucial to understanding the ecosystem functioning. Herein, the ecological processes underpinning community assembly and succession were studied to uncover the respective ecological functions of attached biofilms and suspended biomass in a sequencing batch moving bed biofilm reactor. Compared with suspended biomass, attached biofilms presented higher relative abundances of Nitrospira (2.94 %) and Nitrosomonas (1.25 %), and contributed to 66.89 ± 11.37 % and 68.11 ± 12.72 % of nitrification and denitrification activities, respectively. The microbial source tracking result demonstrated that early formation of suspended biomass was dominated by the seeding effect of detached biofilms in the start-up period (days 0-30), while self-growth of previous suspended biomass was eventually outcompeted the seeding effect when the reactor stabilized (days 31-120). Null model and ecological network analysis further suggested distinctive ecological processes underpinning the differentiation between attached and suspended communities in the same reactor. Specifically, in the start-up period, positive interactions facilitated early formation of attached (73.84 %) and suspended communities (59.41 %), while homogenous selection (88.89 %) and homogenizing dispersal (65.71 %) governed assembly of attached and suspended communities, respectively. When the reactor stabilized, attached and suspended communities showed low composition turnover as reflected by dominant homogenizing dispersal, while they presented distinctive trends of interspecies interactions. This study sheds light on discrepant ecological processes governing community differentiation of attached biofilms and suspended biomass, which would provide ecological insights into the regulation of hybrid ecosystems.