Single-cell transcriptome atlas reveals somatic cell embryogenic differentiation features during regeneration.

Understanding somatic cell totipotency remains a challenge facing scientific inquiry today. Plants display remarkable cell totipotency expression, illustrated by single-cell differentiation during somatic embryogenesis (SE) for plant regeneration. Determining cell identity and exploring gene regulation in such complex heterogeneous somatic cell differentiation have been major challenges. Here, we performed high-throughput single-cell sequencing assays to define the precise cellular landscape and revealed the modulation mode of marker genes during embryogenic differentiation in cotton (Gossypium hirsutum L.) as the crop for biotechnology application. We demonstrated that nonembryogenic calli (NEC) and primary embryogenic calli (PEC) tissues were composed of heterogeneous cells that could be partitioned into four broad populations with six distinct cell clusters. Enriched cell clusters and cell states were identified in NEC and PEC samples, respectively. Moreover, a broad repertoire of new cluster-specific genes and associated expression modules were identified. The energy metabolism, signal transduction, environmental adaptation, membrane transport pathways, and a series of transcription factors were preferentially enriched in cell embryogenic totipotency expression. Notably, the SE-ASSOCIATED LIPID TRANSFER PROTEIN (SELTP) gene dose-dependently marked cell types with distinct embryogenic states and exhibited a parabolic curve pattern along the somatic cell embryogenic differentiation trajectory, suggesting that SELTP could serve as a favorable quantitative cellular marker for detecting embryogenic expression at the single-cell level. In addition, RNA velocity and Scissor analysis confirmed the pseudo-temporal model and validated the accuracy of the scRNA-seq data, respectively. This work provides valuable marker-genes resources and defines precise cellular taxonomy and trajectory atlases for somatic cell embryogenic differentiation in plant regeneration.

New ECG Compression Method for Portable ECG Monitoring System Merged with Binary Convolutional Auto-Encoder and Residual Error Compensation.

In the past few years, deep learning-based electrocardiogram (ECG) compression methods have achieved high-ratio compression by reducing hidden nodes. However, this reduction can result in severe information loss, which will lead to poor quality of the reconstructed signal. To overcome this problem, a novel quality-guaranteed ECG compression method based on a binary convolutional auto-encoder (BCAE) equipped with residual error compensation (REC) was proposed. In traditional compression methods, ECG signals are compressed into floating-point numbers. BCAE directly compresses the ECG signal into binary codes rather than floating-point numbers, whereas binary codes take up fewer bits than floating-point numbers. Compared with the traditional floating-point number compression method, the hidden nodes of the BCAE network can be artificially increased without reducing the compression ratio, and as many hidden nodes as possible can ensure the quality of the reconstructed signal. Furthermore, a novel optimization method named REC was developed. It was used to compensate for the residual between the ECG signal output by BCAE and the original signal. Complemented with the residual error, the restoration of the compression signal was improved, so the reconstructed signal was closer to the original signal. Control experiments were conducted to verify the effectiveness of this novel method. Validated by the MIT-BIH database, the compression ratio was 117.33 and the root mean square difference (PRD) was 7.76%. Furthermore, a portable compression device was designed based on the proposed algorithm using Raspberry Pi. It indicated that this method has attractive prospects in telemedicine and portable ECG monitoring systems.

Genome-Wide cis-Regulatory Element Based Discovery of Auxin-Responsive Genes in Higher Plant.

Auxin has a profound impact on plant physiology and participates in almost all aspects of plant development processes. Auxin exerts profound pleiotropic effects on plant growth and differentiation by regulating the auxin response genes' expressions. The classical auxin reaction is usually mediated by auxin response factors (ARFs), which bind to the auxin response element (AuxRE) in the promoter region of the target gene. Experiments have generated only a limited number of plant genes with well-characterized functions. It is still unknown how many genes respond to exogenous auxin treatment. An economical and effective method was proposed for the genome-wide discovery of genes responsive to auxin in a model plant, Arabidopsis thaliana (A. thaliana). Our method relies on cis-regulatory-element-based targeted gene finding across different promoters in a genome. We first exploit and analyze auxin-specific cis-regulatory elements for the transcription of the target genes, and then identify putative auxin responsive genes whose promoters contain the elements in the collection of over 25,800 promoters in the A. thaliana genome. Evaluating our result by comparing with a published database and the literature, we found that this method has an accuracy rate of 65.2% (309/474) for predicting candidate genes responsive to auxin. Chromosome distribution and annotation of the putative auxin-responsive genes predicted here were also mined. The results can markedly decrease the number of identified but merely potential auxin target genes and also provide useful clues for improving the annotation of gene that lack functional information.

Quantitative metabolome and transcriptome analysis reveals complex regulatory pathway underlying photoinduced fiber color formation in cotton.

As an important plant single cell model and textile application materials, poorly known about fiber color formation in cotton, which is sensitively regulated by environmental signals. Our studies underline the importance of photo signal on sensitive fiber color formation and characterize fiber color early initiation (15 DPA) and late accumulated metabolites (45 DPA) in different lighting condition. The results revealed 236 differential metabolites between control and shading, of which phenylpropanoids metabolites accounted for 20%, including uncharacterized novel metabolites and pathways. Furthermore, the early initiation specific genes respond to the absence of light are highly correlated with phenylpropanoid metabolites related to pigmentation. The current study reveals the complex pathways involving early initiation regulation and late metabolic pathways. In addition, the collection composed of uncharacterized photoinduced metabolites and early initiation signaling/regulatory genes were identified, which are important resources for understanding fiber color formation. This report provides new insight into molecular regulatory and biochemical basis underlying photoinduced fiber color formation in cotton.

A Real Time QRS Detection Algorithm Based on ET and PD Controlled Threshold Strategy.

As one of the important components of electrocardiogram (ECG) signals, QRS signal represents the basic characteristics of ECG signals. The detection of QRS waves is also an essential step for ECG signal analysis. In order to further meet the clinical needs for the accuracy and real-time detection of QRS waves, a simple, fast, reliable, and hardware-friendly algorithm for real-time QRS detection is proposed. The exponential transform (ET) and proportional-derivative (PD) control-based adaptive threshold are designed to detect QRS-complex. The proposed ET can effectively narrow the magnitude difference of QRS peaks, and the PD control-based method can adaptively adjust the current threshold for QRS detection according to thresholds of previous two windows and predefined minimal threshold. The ECG signals from MIT-BIH databases are used to evaluate the performance of the proposed algorithm. The overall sensitivity, positive predictivity, and accuracy for QRS detection are 99.90%, 99.92%, and 99.82%, respectively. It is also implemented on Altera Cyclone V 5CSEMA5F31C6 Field Programmable Gate Array (FPGA). The time consumed for a 30-min ECG record is approximately 1.3 s. It indicates that the proposed algorithm can be used for wearable heart rate monitoring and automatic ECG analysis.

Comparison of Patients' Perceived Quality of Primary Care Between Urban and Rural Community Health Centers in Guangdong, China.

BACKGROUND: A series of reforms were implemented to improve the quality of primary care services in China. This study aims to assess patients' perceived quality of primary healthcare between rural and urban community health centers in Guangdong. METHODS: A cross-sectional survey was conducted from July to December 2015 in Guangdong. We surveyed 1010 respondents who visited either community health centers/stations (CHCs/CHSs) in urban areas or township health centers/rural health stations (THCs/RHSs) in rural areas. A validated Chinese version of the Primary Care Assessment Tool-Adult Short Version (PCAT-AS), representing ten primary care domains, was used to collect information on patients' primary care experiences. A t-test was used for comparison on domain scores and total scores between patients from CHCs/CHSs and THCs/RHSs. An analysis of covariance was employed to compare the adjusted PCAT domain scores and total scores. Multilevel models were used to explore factors associated with PCAT total scores. RESULTS: Overall, patients reported a lower level of experience of community orientation and family centeredness compared to other primary care domains. Patients from THCs/RHSs settings in the rural area reported better primary care experience in four domains, including first contact, accessibility, ongoing care, and community orientation. Higher education background and those with a chronic disease were associated with better primary care experience, after controlling for confounding factors. Patients who preferred primary care institutions when getting sick or used health services more frequently reported better primary care experiences. CONCLUSION: Continued efforts are needed to strengthen primary care performances, particularly in a community orientation and family centeredness. Primary care delivery in CHCs/CHSs settings should be improved in four domains, including first contact, accessibility, ongoing care, and community orientation.

Multi-information fusion neural networks for arrhythmia automatic detection.

BACKGROUND AND OBJECTIVES: . The electrocardiograms (ECGs) are widely used to diagnose a variety of arrhythmias. Generally, the abnormalities of ECG signals mainly consist of ill-shaped ECG beat morphologies and irregular intervals. The ill-shaped ECG beat morphologies represent morphological information, while the irregular intervals denote the temporal information of ECG signals. But it is difficult to utilize morphological information and temporal information simultaneously when dealing with single ECG heartbeats, because RR interval is not contained in a single short heartbeat. Therefore, to handle this problems, a novel Multi-information Fusion Convolutional Bidirectional Recurrent Neural Network (MF-CBRNN) is proposed for arrhythmia automatic detection. METHODS: . The MF-CBRNN is designed with two parallel hybrid branches that can simultaneously focus on the beat-based information in the ECG beats and the segment-based information in the adjacent segments of the beats. A single ECG beat provides the morphological information. At the same time, the adjacent segment of the ECG beat enriches the temporal information, so the two branches are designed to exploit the multiple information contained in ECGs. Furthermore, a combination of convolutional neural networks (CNNs) and a bidirectional long short memory (BLSTM) in each branch is utilized to capture the information from the two inputs. And all the features extracted from the two branches are fused for information aggregation. RESULTS: . To evaluate the performance of the proposed model, the ECG signals from MIT-BIH databases are used for intra-patient and inter-patient paradigms. The proposed model yields an accuracy of 99.56% and an F1-score of 96.40% under the intra-patient paradigm. And it obtains an overall accuracy of 96.77% and F1-score of 77.83% under the inter-patient paradigm. CONCLUSIONS: . Compared with other studies on arrhythmia detection, our method achieves a state-of-the-art performance. It indicates that the proposed model is a promising arrhythmia detection algorithm for computer-aided diagnostic systems.

Arbuscular mycorrhizal fungi (AMF) enhanced the growth, yield, fiber quality and phosphorus regulation in upland cotton (Gossypium hirsutum L.).

We previously reported on the strong symbiosis of AMF species (Rhizophagus irregularis CD1) with the cotton (Gossypium hirsutum L.) which is grown worldwide. In current study, it was thus investigated in farmland to determine the biological control effect of AMF on phosphorus acquisition and related gene expression regulation, plant growth and development, and a series of agronomic traits associated with yield and fiber quality in cotton. When AMF and cotton were symbiotic, the expression of the specific phosphate transporter family genes and P concentration in the cotton biomass were significantly enhanced. The photosynthesis, growth, boll number per plant and the maturity of the fiber were increased through the symbiosis between cotton and AMF. Statistical analysis showed a highly significant increase in yield for inoculated plots compared with that from the non inoculated controls, with an increase percentage of 28.54%. These findings clearly demonstrate here the benefits of AMF-based inoculation on phosphorus acquisition, growth, seed cotton yield and fiber quality in cotton. Further improvement of these beneficial inoculants on crops will help increase farmers' income all over the world both now and in the future.

Satisfaction with service coverage and drug list may influence patients' acceptance of general practitioner contract service: a cross-sectional study in Guangdong, China.

BACKGROUND: General practitioner (GP) system is proved to be effective in over 50 countries worldwide. Guangdong province, as a reform pilot in China, initiated its patient-GP contract service reform in 2014. This study is designed to assess the patients' acceptance of General Practitioners Contract (GPC) reform and explore its influencing factors. METHODS: This survey interviewed 1010 participants from 16 primary health centers (PHCs) chosen from 4 pilot cities in Guangdong during July and December in 2015. Data were collected through face-to-face interviews. The questionnaire was developed to discover the acceptance of GPC and covered three parts: respondents' socio-demographic characteristics, health service utilization, and the patients' assessment of primary health care centers. A binary logistic regression model was used to measure the influencing factors of respondents' acceptance of GPC policy. RESULTS: A total of 611(60.5%) participants accepted GPC policy. Compared to patients visited PHCs over 7 times in the previous year, those visited PHCs fewer times reported lower acceptance of GPC policy (OR:0.68, 95% CI:0.49-0.96 for visits ≤3 times and OR:0.57, 95% CI:0.38-0.84 for visits = 4-6 times). Patients' satisfaction with medical service coverage was positively associated with patients' acceptance of GPC (OR: 1.72, 95% CI:1.01-3.98 for the satisfied versus the dissatisfied; OR: 1.38, 95% CI:0.92-3.30 for neutral versus the dissatisfied), and the satisfaction with drug list also positively influenced patients' acceptance of GPC policy (OR: 1.44, 95% CI:1.26-2.73 for the satisfied versus the dissatisfied; OR:1.61, 95% CI:1.36-2.99 for neutral versus the dissatisfied). Meanwhile, age and education had positive impacts on the acceptance of the GPC policy. CONCLUSION: This study finds out that patients' satisfaction with medical service coverage and drug list are the influencing factors for the acceptance of GPC policy. Therefore, improvement of medical service accessibility such as better follow-up of patients with chronic diseases and enhanced referral service, as well as the expansion of drug list, will improve patients' acceptance of GPC policy. It also finds that patients using more primary health service are inclined to accept GPC policy, so more attractive and high-quality service should be provided in PHCs.

[Identification of overexpressed genes induced by nitric oxid in human esophageal carcinoma cell line].

BACKGROUND & OBJECTIVE: It is possible that some important genes were overexpressed in esophageal carcinoma cells induced by nitric oxid(NO), but it is not yet clear which the genes are. The objective of this study was to separate and identify the overexpressive genes induced by NO in EC109 esophageal carcinoma cell line. METHODS: Overexpression of the genes induced by nitric oxid(NO) in EC109 esophageal carcinoma cell line was determined by using suppression subtractive hybridization(SSH). Sixty-nine positive clones were obtained by EcoR I digesting, and overexpressive genes were screened by reverse mRNA dot blot and further confirmed by Northern blot analysis. The nucleotide of their expressed sequence tag(EST) was sequenced and analyzed by using NCBI database. RESULTS: The two mitochondrial DNA coding genes, ND-4L and ND-4, were identified. The ND-4L and ND-4 genes were overexpressed distinctly in the EC109 cell induced by NO. The three point mutations were discovered in a fragment(10,736-11,449) of ND-4L and ND-4 genes. CONCLUSION: NO can induce overexpression of the ND-4L and ND-4 genes which is coded by mitochondrial DNA. These results provide a new idea for further exploring the mechanism of NO effecting on carcinoma cells.