Miracle-dz/UniFAR-FTUs huggingface dataset:
Cell-connectivity-guided trajectory inference from single-cell data

Abstract Motivation Single-cell RNA-sequencing enables cell-level investigation of cell differentiation, which can be modelled using trajectory inference methods. While tremendous effort has been put into designing these methods, inferring accurate trajectories automatically remains difficult. Therefore, the standard approach involves testing different trajectory inference methods and picking the trajectory giving the most biologically sensible model. As the default parameters are often suboptimal, their tuning requires methodological expertise. Results We introduce Totem, an open-source, easy-to-use R package designed to facilitate inference of tree-shaped trajectories from single-cell data. Totem generates a large number of clustering results, estimates their topologies as minimum spanning trees, and uses them to measure the connectivity of the cells. Besides automatic selection of an appropriate trajectory, cell connectivity enables to visually pinpoint branching points and milestones relevant to the trajectory. Furthermore, testing different trajectories with Totem is fast, easy, and does not require in-depth methodological knowledge. Availability and implementation Totem is available as an R package at https://github.com/elolab/Totem.
Research on dynamic allocation method for airport gate based on capacity relaxation

With the increasing level of national economy, the throughput of domestic flights is increasing. Restricted by the limited airport gates, the reasonable allocation of parking spaces becomes the key to improve the airport operation efficiency. Therefore, a dynamic adjustment model of airport gate allocation is proposed, and the effective results of the average occupancy ratio and the low number of adjustments of airport gates are obtained by applying the gradient descent method for dynamic adjustment, while the data of two peak hours of airport occupancy of a domestic hub airport are used for simulation and the dynamic optimization of the allocation of airport gates during these hours. The results show that the dynamic adjustment of airport gates scheme achieves better results with even allocation of airport gates, increasing slack airport gates and adapting to the situation under different flight densities, which plays the significance of rational utilization of airport gates, reducing flight delays and improving airport operation efficiency.
The Interconnection between Carotid Intima–Media Thickness and Obesity: Anthropometric, Clinical and Biochemical Correlations

Background and Objectives: Carotid intima–media thickness (CIMT) and obesity are considered independent determinants of cardio- and cerebrovascular events. The aim of our study was to investigate the effect of obesity on CIMT and to define which traditional cardiovascular risk factors correlate the most with CIMT values in patients with obesity. Materials and Methods: Anthropometric measurements were collected for the whole study group, as well as body composition and blood pressure data, and biochemical blood analyses were also performed. Results: Although our study group was significantly older according to vascular compared with chronological age, the mean CIMT values were lower when compared with the reference values. We found a statistically significant correlation of CIMT with chronological and vascular age, systolic blood pressure, fasting glucose, total cholesterol and triglyceride levels, waist-to-hip ratio, waist circumference, body muscle mass and skeletal muscle mass index. Atherosclerotic Cardiovascular Disease (ASCVD) risk assessment and SCORE (Systematic COronary Risk Evaluation) showed significant positive correlations, but there was only a weak correlation of ASCVD with CIMT. Conclusions: To deduce, since no diagnostic tool currently includes body weight as an individual risk factor, further trials are highly needed to determine if SCORE, SCORE2, ASCVD risk assessment or CIMT would be the most accurate and relevant diagnostic tool for prediction of risk for future CV events in patients with obesity.
A stochastic proximal alternating minimization algorithm for nonnegative matrix decomposition

The stochastic Proximal Alternating Linearized Minimization (PALM) algorithm is an effective method for solving non-smooth non-convex problems. In this paper, we extend this method to solve non-negative matrix factorization with finite and structured constraints. We propose a new approach for calculating stochastic gradients and provide the theoretical analysis on the presented stochastic proximal alternating algorithm. The new algorithm is proved to have linear convergence rates with variance-reduction stochastic gradient estimators such as SAG, SAGA and SVRG. In addition, we conduct numerical experiments to compare the performance of three stochastic gradient estimators, and finally obtain that the algorithms with SVRG gradient estimators is shown to be more effective for solving nonnegative matrix decomposition problems.
LH/hCG Regulation of Circular RNA in Mural Granulosa Cells during the Periovulatory Period in Mice

Ovarian follicles undergo a series of dynamic changes following the ovulatory surge of luteinizing hormone including cumulus expansion, oocyte maturation, ovulation, and luteinization. Post-transcriptional gene regulatory events are critical for mediating LH follicular responses, and among all RNA isoforms, circular RNA (circRNA) is one of the most abundant forms present in cells, yet they remain the least studied. Functionally, circRNA can act as miRNA sponges, protein sponges/decoys, and regulators of transcription and translation. In the context of ovarian follicular development, the identity and roles of circRNA are relatively unknown. In the present study, high throughput RNA sequencing of granulosa cells immediately prior to and 4-h after the LH/hCG surge identified 42,381 circRNA originating from 7712 genes. A total of 54 circRNA were identified as differentially expressed between 0-h and 4-h time points (Fold Change ± 1.5, FDR ≤ 0.1), among them 42 circRNA were upregulated and 12 circRNA were downregulated. All differentially expressed circRNA between the 0-h and 4-h groups were subjected to circinteractome analysis and identified networks of circRNA-protein and circRNA-miRNA were further subjected to “micro-RNA target filter analysis” in Ingenuity Pathway Analyses, which resulted in the identification of miRNA targeted mRNAs. A comparison of these circRNA target mRNAs with LH-induced mRNAs identified Runx2, Egfr, Areg, Sult1el, Cyp19a1, Cyp11a1, and Hsd17b1 as targets of circKif2, circVcan, circMast4, and circMIIt10. These newly identified LH/hCG-induced circRNA, their target miRNA and protein networks provide new insights into the complex interactions associated with periovulatory follicular development.
A Noninvasive Blood Glucose Estimation System Using Dual-Channel PPGs and Pulse-Arrival Velocity

Diabetes is a chronic metabolic disorder that requires individuals with diabetes to perform “finger stick blood sugar tests” multiple times a day. This invasive method is not capable of continuously monitoring a diabetic patient’s blood glucose levels and can be painful and inconvenient in the long term. In our study, we developed a noninvasive blood glucose estimation method using dual-channel photoplethysmography (PPG) combined with pulse arrival velocity (PAV). The dual-channel PPG was chosen based on the advantages of 530 nm PPG in blood flow measurement and 1550 nm infrared light in blood glucose concentration detection. In addition to testing various amplitude features in PPG at different fiducial points (systolic peak, dicrotic notch, and diastolic peak), we also examined the amplitude ratio between the two PPG channels (530 nm green-light PPG and 1550 nm infrared PPG), as well as PAV, for noninvasive blood glucose estimation. In this study, we recruited 18 healthy subjects (14 males and four females; aged 29.4 ± 7.87 years old). The oral glucose tolerance test (OGTT) experiment was conducted on each participant to manipulate the blood glucose level. Each parameter for blood glucose estimation was examined using partial ${F}$ -test to assess its impact on blood glucose estimation. We found that combining the amplitude ratio of the dual-channel PPG with PAV resulted in the best blood glucose estimation results, with estimation results falling within Zone A in the Clarke error grid analysis (CEGA) with a 100% success rate, and the root-mean-square-error (RMSE) being 7.46 ± 2.43 mg/dL.
A THEORY OF STRUCTURAL CHANGE, HOME PRODUCTION AND LEISURE

Why do agents consume more services relative to goods as income grows? We present a theory of structural change assuming that a representative household satisﬁes ﬁnal needs by means of two home-production functions in time and either goods or services from the market. When calibrating the model to U.S. data, roughly half of structural change is accounted for by technological change allowing services to display a larger time saving than goods in satisfying ﬁnal needs. Also, even if preferences are homothetic, the calibrated model generates endogenous income eﬀects, which account for the remaining structural change generated by the model
An End-to-End Contrastive License Plate Detector

As a unique identity of vehicle, License Plate (LP) facilitates the intelligent transportation in many fields, such as traffic enforcement, intelligent transportation dispatching, etc. Recently, the LP detectors are trained by supervised learning which is directly guided by manual annotations and lacks the use of visual knowledge in image content, limiting the further development of detection performance. Inspired by the contrast and comparison in perception of human beings, a contrastive learning method is introduced into license plate detection task and we propose an end-to-end Contrastive License Plate Detector (CLPD). In CLPD, a special contrastive triad for contrastive learning is designed which aims to decouple the foregrounds and backgrounds. Based on this triad, a contrastive learning branch is introduced into the license plate detection pipeline to prompt the feature expression ability of backbone and extracting more discriminative features for detection. This contrastive learning branch is jointly trained with supervised learning branch for detection and it is only used in training, keeping the efficiency in inference. The experiment results show that the proposed CLPD improves the detection accuracy compared to baselines and other license plate detectors significantly on three datasets. The ablation studies further explore the potential of CLPD. In addition, the proposed CLPD has generalization to improve the performance on different baselines. And the visualization results in latent space verify our proposed CLPD aggregates features tightly and extracts discriminative features effectively.
Apple object detection in natural environment based on YOLO v5

Apple's recognition and positioning is an important part of the picking robot system is also a key function. Apple target recognition in natural environment is affected by environmental factors such as weather, branches and leaves, which increase the difficulty of apple recognition and reduce the accuracy of recognition. In recent years, artificial intelligence computer vision technology has been able to accurately and quickly detect objects through continuous development. Based on deep learning, an apple detection method in natural environment based on YOLO v5s deep learning was proposed. Images of apple in natural environment were tagged and sent to the YOLO v5s target detection network with SE attention mechanism for network model training. After multiple rounds of network model training, the accuracy rate of the final model is 86.0%, the recall rate is 93.6%, the average accuracy is 90.9%, the model size is 15.6MB, and the detection speed is 84.0 frames /s. The results show that YOLO v5s-SE can accurately and quickly realize the target detection of apple in natural environment, and the model has high robustness and relative fast recognition speed.
Application of real-time polymerase chain reaction for the quantitation of interleukin-1beta mRNA upregulation in brain ischemic tolerance.

Differential gene expression plays an important role in normal development and pathophysiological conditions. The accurate quantitation of mRNA expression is critical to assess the differential gene expression. While a number of techniques, such as Northern analysis, (semi-)quantitative reverse transcription polymerase chain reaction (RT-PCR) and in situ hybridization, are available to measure the levels of mRNA expression, certain limitations exist, including the insensitive and inaccurate quantitation of mRNA expressed at low abundance. In the present study, we describe the application of a recently developed TaqMan real-time quantitative RT-PCR for the detection of interleukin-1beta (IL-1beta) mRNA expression in rat cortical tissue after a short duration of ischemia (i.e., ischemic preconditioning). The principle of the TaqMan real-time detection is based on the fluorogenic 5' nuclease assay that allows simple and rapid quantitation of a target sequence during the extension phase of PCR amplification. Using a cloned plasmid DNA as a standard and normalizing RNA samples with a housekeeping gene for the TaqMan real-time PCR, we detected the significant induction in absolute copy numbers of IL-1beta mRNA in the ipsilateral cortex after preconditioning, suggesting a potential role of this inflammatory cytokine in ischemic brain tolerance.
MicroRNA-323-5p Involved in Dexmedetomidine Preconditioning Impart Neuroprotection

Background and Objectives: Cerebral ischemia is one of the major preoperative complications. Dexmedetomidine is a well-known sedative–hypnotic agent that has potential organ-protective effects. We examine the miRNAs associated with preconditioning effects of dexmedetomidine in cerebral ischemia. Materials and Methods: Transient infarcts were induced in mice via reperfusion after temporary occlusion of one side of the middle cerebral artery. A subset of these mice was exposed to dexmedetomidine prior to cerebral infarction and miRNA profiling of the whole brain was performed. We administered dexmedetomidine and miRNA-323-5p mimic/inhibitor to oxygen–glucose deprivation/reoxygenation astrocytes. Additionally, we administered miR-323-5p mimic and inhibitor to mice via intracerebroventricular injection 2 h prior to induction of middle cerebral artery occlusion. Results: The infarct volume was significantly lower in the dexmedetomidine-preconditioned mice. Analysis of brain samples revealed an increased expression of five miRNAs and decreased expression of three miRNAs in the dexmedetomidine-pretreated group. The viability of cells significantly increased and expression of miR-323-5p was attenuated in the dexmedetomidine-treated oxygen–glucose deprivation/reoxygenation groups. Transfection with anti-miR-323-5p contributed to increased astrocyte viability. When miRNA-323-5p was injected intraventricularly, infarct volume was significantly reduced when preconditioned with the miR-323-5p inhibitor compared with mimic and negative control. Conclusions: Dexmedetomidine has a protective effect against transient neuronal ischemia–reperfusion injury and eight specific miRNAs were profiled. Also, miRNA-323-5p downregulation has a cell protective effect under ischemic conditions both in vivo and in vitro. Our findings suggest the potential of the miR-323-5p inhibitor as a therapeutic agent against cerebral infarction.
A proposal for more efficient travel behaviour change interventions

Travel behaviour change (TBC) calls for a market-oriented approach to target people's mentalities and lifestyles and make invested resources efficient. Attitudinal segmentation enables focusing on attitudes that may lead to behavioural change. A sound theoretical background is provided by the Theory of Planned Behaviour and well-conceived interventions can be based on the insights of social psychology and marketing. In this framework, a comprehensive effort was undertaken in response to a deficit of detailed initiatives recording. Focusing on environment and physical activity, a travellersl typology was extracted and addressed with a specific intervention scheme fed by detailed literature review. The experimental design adopted dealt with 37 participants in two groups (intervention and control), using pre and post measurement. The intervention raised problem awareness, other means use and physical activity uptake. This paper aims to present the methodological steps of a TBC intervention scheme, proving efficiency evidence and giving pace for a broader implementation.
A Three-Dimensional Model of the Antarctic Ice Sheet

A preliminary version of a three-dimensional ice-sheet model for later use in climate models, but excluding ice shelves and basal sliding, is presented and applied to the Antarctic ice sheet. In the model, the three-dimensional fields of velocity and temperature are calculated in the coupled mode, and the temperature equation is integrated for 150 000 years; the shape of the Antarctic ice sheet remains fixed. The results from the model are consistent with a stationary state in the central parts of the Antarctic ice sheet, but not in marginal areas, where the flow in the model is too small. Including a parameterized form of basal sliding that is dependent on the water pressure is likely to improve the situation.
Analysis of the Student Numerical Literacy in Completing the Division Count and Multiplication Operation of the 6th Grade Elementary School Round Number

The background of this study is that students are unable to solve the problems and problems of daily life and which of the students associated with literacy, are unable to think critically and be able to reason in understanding the concept of division operations and multiplication integers. The aim of this study was to describe numerical literacy in the division and multiplication operations of integers in 6th grade elementary school. Research type use descriptive with a qualitative approach. Research instrument consist of interview instrument, observation instrument, and test subject. Research has found no significant obstacle, the study subject can complete most numerical literacy components on the test issue. Research subjects has not been able to dominate numerical literacy of patterns and statistic intact, but some of the components appearing on the subject have not been fully resolved.
EXPLORING THE PEDAGOGICAL ASPECTS OF MICROLEARNING IN EDUCATIONAL SETTINGS: A SYSTEMATIC LITERATURE REVIEW

Purpose - Technology has revolutionized education, leading to innovative learning techniques like microlearning. Microlearningis gaining popularity in higher education and corporate settings for its student-centred approach and well-planned modules. However,its pedagogical design is complex, requiring a systematic review of studies to identify effective practices. The purpose of this study is to conduct a systematic literature review to identify effective practices of microlearning in teaching and learning in higher education between 2014 and 2023. The study aims to analyse the pedagogical design of microlearning in educational settings. Methodology - The study follows the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines.A systematic literature review was conducted to identify, evaluate, interpret, and analyse available studies on the practices of microlearning. The analysis focused on the research question and identified themes related to the pedagogical design of microlearning. Findings - The analysis revealed two final themes: the design of microlearning content and instructional flow. The design of microlearning content focused on developing microlearning material, while instructional flow focused on organizing content learning.These findings provide insights into effective practices for curriculum designers and instructors in designing and developing microlearning strategies in teaching and learning. Significance - The findings of this study have significant implications for educators and curriculum designers. The study highlights theimportance of considering both the design of microlearning content and instructional flow in creating engaging and effective microlearning experiences for students in higher education. By incorporating these findings, educators and curriculum designers can enhance the quality of microlearning strategies in teaching and learning.
Research methods on telecom user churn

The prediction of telecom customer churn is of great significance for the telecommunications industry, and effective and accurate predictions can make countermeasures for the status of customer churn. In order to make more effective predictions, we aim to investigate the most suitable machine learning-based algorithm for the task. In this article, a dataset of telecom customer churn was selected, which includes over 1000 sets of data and involves a comprehensive range of influencing factors. In order to find the algorithm with the best prediction performance, this paper implements various algorithms, such as XGBoost, random forest, decision tree and Adaboost, to analyze and process the data set. By comparing several statistical metrics, it is finally found that XGBoost is the most suitable algorithm for processing this data set, and random forest is not a good choice to process the data set.
Three-Phase Asynchronous Motor Fault Diagnosis Using Attention Mechanism and Hybrid CNN-MLP By Multi-Sensor Information

Single-signal-driven fault diagnosis has been widely applied in motor fault diagnosis, but it cannot meet the diagnostic requirements of complex motor systems. This study proposes a motor fault diagnosis method using attention mechanism (AM) and hybrid CNN-MLP by multi-sensor information. Firstly, Fast Fourier transform and continuous wavelet transform are performed on different signals to obtain the corresponding frequency domain feature information and wavelet time-frequency map images. A hybrid CNN-MLPAM model is used to extract features from spectral feature information and wavelet time-frequency images, respectively, and is trained to obtain preliminary classification results. Finally, a dynamic weight distribution vector is used to obtain the final diagnosis. The proposed method is verified by current, and vibration signals. The results show that the method can dynamically evaluate the sensitivity of different detection signals to different faults. The proposed method is more accurate and stable in fault diagnosis than the traditional method that relies solely on vibration signals. Under the consideration of time cost and diagnostic accuracy, the proposed CNN-MLPAM has higher diagnostic performance compared with CNN-RNNAM and CNN-ELMAM.