Miracle-dz/UniFAR-FTUs huggingface dataset:
Image-based methods for phase estimation, gating and temporal super-resolution of cardiac ultrasound

Objective: Ultrasound is an effective tool for rapid noninvasive assessment of cardiac structure and function. Determining the cardiorespiratory phases of each frame in the ultrasound video and capturing the cardiac function at a much higher temporal resolution are essential in many applications. Fulfilling these requirements is particularly challenging in preclinical studies involving small animals with high cardiorespiratory rates, requiring cumbersome and expensive specialized hardware. Methods: We present a novel method for the retrospective estimation of cardiorespiratory phases directly from the ultrasound videos. It transforms the videos into a univariate time series preserving the evidence of periodic cardiorespiratory motion, decouples the signatures of cardiorespiratory motion with a trend extraction technique, and estimates the cardiorespiratory phases using a Hilbert transform approach. We also present a robust nonparametric regression technique for respiratory gating and a novel kernel-regression model for reconstructing images at any cardiac phase facilitating temporal superresolution. Results: We validated our methods using two-dimensional echocardiography videos and electrocardiogram (ECG) recordings of six mice. Our cardiac phase estimation method provides accurate phase estimates with a mean-phase-error range of 3%–6% against ECG derived phase and outperforms three previously published methods in locating ECGs R-wave peak frames with a mean-frame-error range of 0.73–1.36. Our kernel-regression model accurately reconstructs images at any cardiac phase with a mean-normalized-correlation range of 0.81–0.85 over 50 leave-one-out-cross-validation rounds. Conclusion and significance: Our methods can enable tracking of cardiorespiratory phases without additional hardware and reconstruction of respiration-free single cardiac-cycle videos at a much higher temporal resolution.
Frequency Network Analysis of Heart Rate Variability for Obstructive Apnea Patient Detection

Obstructive sleep apnea (OSA) is a popular sleep disorder. Traditional OSA diagnosis methods are cumbersome and expensive, which bring inconvenience for patient diagnosis and heavy workload for physician. Automatically identifying OSA patients from electrocardiogram (ECG) records is important for clinical diagnosis and treatment. In this paper, a new method based on the frequency and network domains is proposed to automatically recognize OSA patients with nocturnal ECG records. First, each RR-interval (beat to beat heart rate) series was divided into segments. By calculating the power spectral density (PSD) of heart rate variability segment with Lomb–Scargle method, the dynamic time warping (DTW) distance was used to evaluate the similarity (dissimilarity) of the lower frequency in the PSD series, then the DTW distance matrix was transformed to a binary matrix, and then network metrics were calculated to discriminate OSA patients with healthy subjects. The new method was tested with data of 389 subjects collected from two public databases that consist of normal subjects without OSA (apnea–hypopnea index, AHI<inline-formula> <tex-math notation="LaTeX">$\leq$</tex-math></inline-formula>5) and OSA patients (AHI<inline-formula> <tex-math notation="LaTeX">$>$</tex-math></inline-formula>5). Results show that a single network metric (local clustering coefficient) can recognize OSA patients with 90.1% accuracy, 88.29% sensitivity, and 90.5% specificity, and confirm the potential of using the ECG records for OSA patients recognition.
Joint Learning of Multiple Differential Networks With Latent Variables

Graphical models have been widely used to learn the conditional dependence structures among random variables. In many controlled experiments, such as the studies of disease or drug effectiveness, learning the structural changes of graphical models under two different conditions is of great importance. However, most existing graphical models are developed for estimating a single graph and based on a tacit assumption that there is no missing relevant variables, which wastes the common information provided by multiple heterogeneous data sets and underestimates the influence of latent/unobserved relevant variables. In this paper, we propose a joint differential network analysis (JDNA) model to jointly estimate multiple differential networks with latent variables from multiple data sets. The JDNA model is built on a penalized D-trace loss function, with group lasso or generalized fused lasso penalties. We implement a proximal gradient-based alternating direction method of multipliers to tackle the corresponding convex optimization problems. Extensive simulation experiments demonstrate that JDNA model outperforms state-of-the-art methods in estimating the structural changes of graphical models. Moreover, a series of experiments on several real-world data sets have been performed and experiment results consistently show that our proposed JDNA model is effective in identifying differential networks under different conditions.
Golgi Phosphoprotein 3 Promotes Wls Recycling and Wnt Secretion in Glioma Progression

Background/Aims: Golgi phosphoprotein 3 (GOLPH3) plays pro-malignancy roles in several types of cancer. However, the molecular mechanism underlying GOLPH3 promoting tumor progression remains poorly understood. Methods: The expression of GOLPH3 and Wntless (Wls) in glioma tissues was examined by western blotting and immunohistochemistry. EdU incorporation assay and colony formation assay was used to examine the cell growth ability. The effect of GOLPH3 on Wls recycling, Wnt secretion and β-catenin activity was detected using western blotting, immunofluorescence, RT-PCR, ELISA or luciferase assay. Results: The protein levels of GOLPH3 and Wls were upregulated and positively correlated with each other in human glioma tissues. The promoting effect of GOLPH3 on glioma cell proliferation was partially mediated by Wls. In addition, GOLPH3 interacted with Wls and GOLPH3 down-regulation drove Wls into lysosome for degradation, inhibiting its recycling to golgi and the plasma membrane. Importantly, GOLPH3 down-regulation inhibited Wnt2b secretion and decreased β-catenin level and transcription activity. Conclusions: This study provides a brand new evidence that GOLPH3 promotes glioma cell proliferation by facilitating Wls recycling and Wnt/β-catenin signaling. Our findings suggest a rationale for targeting the GOLPH3-Wls-Wnt axis as a promising therapeutic approach for glioblastoma.
Circadian clock rhythms in different adipose tissue model systems

ABSTRACT Circadian clock-controlled 24-h oscillations in adipose tissues play an important role in the regulation of energy homeostasis, thus representing a potential drug target for prevention and therapy of metabolic diseases. For pharmacological screens, scalable adipose model systems are needed that largely recapitulate clock properties observed in vivo. In this study, we compared molecular circadian clock regulation in different ex vivo and in vitro models derived from murine adipose tissues. Explant cultures from three different adipose depots of PER2::LUC circadian reporter mice revealed stable and comparable rhythms of luminescence ex vivo. Likewise, primary pre- and mature adipocytes from these mice displayed stable luminescence rhythms, but with strong damping in mature adipocytes. Stable circadian periods were also observed using Bmal1-luc and Per2-luc reporters after lentiviral transduction of wild-type pre-adipocytes. SV40 immortalized adipocytes of murine brown, subcutaneous and epididymal adipose tissue origin showed rhythmic mRNA expression of the core clock genes Bmal1, Per2, Dbp and REV-erbα in pre- and mature adipocytes, with a maturation-associated increase in overall mRNA levels and amplitudes. A comparison of clock gene mRNA rhythm phases revealed specific changes between in vivo and ex vivo conditions. In summary, our data indicate that adipose culture systems to a large extent mimic in vivo tissue clock regulation. Thus, both explant and cell systems may be useful tools for large-scale screens for adipose clock regulating factors.
Learning Rates of Regularized Regression With Multiple Gaussian Kernels for Multi-Task Learning

This paper considers a least square regularized regression algorithm for multi-task learning in a union of reproducing kernel Hilbert spaces (RKHSs) with Gaussian kernels. It is assumed that the optimal prediction function of the target task and those of related tasks are in an RKHS with the same but with unknown Gaussian kernel width. The samples for related tasks are used to select the Gaussian kernel width, and the sample for the target task is used to obtain the prediction function in the RKHS with this selected width. With an error decomposition result, a fast learning rate is obtained for the target task. The key step is to estimate the sample errors of related tasks in the union of RKHSs with Gaussian kernels. The utility of this algorithm is illustrated with one simulated data set and four real data sets. The experiment results illustrate that the underlying algorithm can result in significant improvements in prediction error when few samples of the target task and more samples of related tasks are available.
Predicting Athlete Ground Reaction Forces and Moments From Spatio-Temporal Driven CNN Models

The accurate prediction of three-dimensional (3-D) ground reaction forces and moments (GRF/Ms) outside the laboratory setting would represent a watershed for on-field biomechanical analysis. To extricate the biomechanist's reliance on ground embedded force plates, this study sought to improve on an earlier partial least squares (PLS) approach by using deep learning to predict 3-D GRF/Ms from legacy marker based motion capture sidestepping trials, ranking multivariate regression of GRF/Ms from five convolutional neural network (CNN) models. In a possible first for biomechanics, tactical feature engineering techniques were used to compress space-time and facilitate fine-tuning from three pretrained CNNs, from which a model derivative of ImageNet called “CaffeNet” achieved the strongest average correlation to ground truth GRF/Ms <inline-formula><tex-math notation="LaTeX">$r(F_{\text{mean}})$</tex-math></inline-formula> 0.9881 and <inline-formula><tex-math notation="LaTeX">$r(M_{\text{mean}})$</tex-math></inline-formula> 0.9715 (<inline-formula><tex-math notation="LaTeX">$r\text{RMSE}$</tex-math></inline-formula> 4.31 and 7.04%). These results demonstrate the power of CNN models to facilitate real-world multivariate regression with practical application for spatio-temporal sports analytics.
Clinical outcome of kidney transplantation in HIV-infected recipients: a retrospective study

Kidney transplantation is a safe and effective option for HIV-positive (HIV+) patients. We conducted a retrospective study on HIV+ kidney transplant recipients who underwent transplantation from March 2008 to September 2016. Inclusion criteria for transplantation were CD4+ T-cell count ≥200 per mm3 and undetectable HIV load. The current study reports the outcome of 19 HIV+ recipients, mostly of Caucasian origin (79%) with a median age of 50 years (interquartile range [IQR], 42–52), who were followed up for a median period of 2.4 years (IQR, 1.2–4.6) after transplantation. Compared with HIV-negative (HIV−) controls, HIV+ recipients had similar one- and three-year graft and patient survival, but significantly lower five-year patient survival (P = 0.03). The differences in graft outcome became less evident with the analysis of death-censored graft survival rates. Cumulative incidence of allograft rejection at one year was 32.9%. Rates of infections were not particularly elevated and HIV replication remained well controlled in all but one patient. A high prevalence of metabolic and endocrine complications (68%) was reported after transplantation. Further studies are needed to evaluate long-term outcomes of HIV+ recipients who underwent kidney transplantation.
Medical Image Synthesis with Deep Convolutional Adversarial Networks

Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical image synthesis can be of great benefit by estimating a desired imaging modality without incurring an actual scan. In this paper, we propose a generative adversarial approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate a target image given a source image. To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN. Moreover, the FCN is designed to incorporate an image-gradient-difference-based loss function to avoid generating blurry target images. Long-term residual unit is also explored to help the training of the network. We further apply Auto-Context Model to implement a context-aware deep convolutional adversarial network. Experimental results show that our method is accurate and robust for synthesizing target images from the corresponding source images. In particular, we evaluate our method on three datasets, to address the tasks of generating CT from MRI and generating 7T MRI from 3T MRI images. Our method outperforms the state-of-the-art methods under comparison in all datasets and tasks.
An Optimization Algorithm to Build Low Congestion Multi-Ring Topology for Optical Network-on-Chip

SUMMARY Ring-based topology is popular for optical network-on-chip. However, the network congestion is serious for ring topology, especially when optical circuit-switching is employed. In this paper, we proposed an algorithm to build a low congestion multi-ring architecture for optical network-on-chip without additional wavelength or scheduling overhead. A network congestion model is established with new network congestion factor deﬁned. An algorithm is developed to optimize the low congestion multi-ring topology. Finally, a case study is shown and the simulation results by OPNET verify the superiority over the traditional ONoC architecture.
The chaperone‐like sodium phenylbutyrate improves factor IX intracellular trafficking and activity impaired by the frequent p.R294Q mutation

Essentials Missense mutations often impair protein folding, and thus intracellular trafficking and secretion. Cellular models of severe type I hemophilia B were challenged with chaperone‐like compounds. Sodium phenylbutyrate improved intracellular trafficking and secretion of the frequent p.R294Q. The increased coagulant activity levels (∼3%) of p.R294Q would ameliorate the bleeding phenotype.
Adaptive Formation Control of Cooperative Teleoperators With Intermittent Communications

Most research so far in teleoperation control has assumed that all information is transmitted continuously. Unfortunately, the damaged and electromagnetic interfered line cause communication link failure. In addition, the unreliable link further leads to port data congestion. The data packet will be discarded when the buffer overflows. Consequently, it is unknown whether stability of the teleoperator could be guaranteed in the presence of intermittent communications. In order to overcome these drawbacks, in this paper, we provide a solution to the formation control problem of a single-master–multislave teleoperator in the situation where each robot is allowed to communicate with its neighbors only at some irregular discrete time instants. The relationship among control gains, topology, and maximum-allowable connected interval is presented. Simulations are performed to show the validity of our proposed approach.
Recurrent or De Novo Allograft Steatosis and Long-term Outcomes After Liver Transplantation

Background Hepatic steatosis is strongly associated with cardiovascular disease in the general population. Whether recurrent or de novo, it can occur in the allograft, but the impact on survival and long-term clinical outcomes remains unclear. In this study, we aim to determine both the frequency and impact of allograft steatosis on long-term posttransplant outcomes. Methods A retrospective review of 588 adult liver transplant (LT) recipients (1999-2006) was performed. Cox regression analysis (time-dependent) was used to evaluate differences in time to steatosis post-LT, patient survival, and cardiovascular outcomes. Results Mean age 51.9 ± 10.6 years, 64.6% males, underlying nonalcoholic steatohepatitis (NASH) (9.4%), previous tobacco (52%), pre-LT diabetes mellitus (30.3%), pre-LT hypertension (23.2%), and known cardiovascular disease (9.7%). Overall, 254 recipients developed allograft steatosis (at 10 years: 77.6% NASH recipients, 44.7% Non-NASH recipients). Risk factors for allograft steatosis were female sex (hazard ratio [HR], 1.47; 95% confidence interval [CI], 1.09-2.00; P = 0.014), hepatitis C virus diagnosis (HR, 2.49; 95% CI, 1.77-3.94; P < 0.001), and time-dependent BMI (per unit: HR, 1.08; 95% CI, 1.05-1.10; P < 0.001). Allograft steatosis was not associated with post-LT survival (P = 0.25) nor cardiovascular events (HR, 1.08; 95% CI, 0.73-1.59; P = 0.70). Underlying NASH associated with cardiovascular events (HR, 2.04; 95% CI, 1.37-3.04; P < 0.001). Conclusions Allograft steatosis is common but not associated with survival or cardiovascular events in this study. Larger prospective studies are needed to better define the natural history of allograft steatosis.
Prevalence of chronic comorbidities in dengue fever and West Nile virus: A systematic review and meta-analysis

Background Flavivirus diseases such as dengue fever (DENV), West Nile virus (WNV), Zika and yellow fever represent a substantial global public health concern. Preexisting chronic conditions such as cardiovascular diseases, diabetes, obesity, and asthma were thought to predict risk of progression to severe infections. Objective We aimed to quantify the frequency of chronic comorbidities in flavivirus diseases to provide an estimate for their prevalence in severe and non-severe infections and examine whether chronic diseases contribute to the increased risk of severe viral expression. Methods We conducted a comprehensive search in PubMed, Ovid MEDLINE(R), Embase and Embase Classic and grey literature databases to identify studies reporting prevalence estimates of comorbidities in flavivirus diseases. Study quality was assessed with the risk of bias tool. Age-adjusted odds ratios (ORs) were estimated for severe infection in the presence of chronic comorbidities. Results We identified 65 studies as eligible for inclusion for DENV (47 studies) and WNV (18 studies). Obesity and overweight (i.e., BMI> 25 kg/m2, prevalence: 24.5%, 95% CI: 18.6–31.6%), hypertension (17.1%, 13.3–21.8%) and diabetes (13.3%, 9.3–18.8%) were the most prevalent comorbidities in DENV. However, hypertension (45.0%, 39.1–51.0%), diabetes (24.7%, 20.2–29.8%) and heart diseases (25.6%, 19.5–32.7%) were the most prevalent in WNV. ORs of severe flavivirus diseases were about 2 to 4 in infected patients with comorbidities such as diabetes, hypertension and heart diseases. The small number of studies in JEV, YFV and Zika did not permit estimating the prevalence of comorbidities in these infections. Conclusion Higher prevalence of chronic comorbidities was found in severe cases of flavivirus diseases compared to non-severe cases. Findings of the present study may guide public health practitioners and clinicians to evaluate infection severity based on the presence of comorbidity, a critical public health measure that may avert severe disease outcome given the current dearth of clear prevention practices for some flavivirus diseases.
Deep Learning for Plant Species Classification Using Leaf Vein Morphometric

An automated plant species identification system could help botanists and layman in identifying plant species rapidly. Deep learning is robust for feature extraction as it is superior in providing deeper information of images. In this research, a new CNN-based method named D-Leaf was proposed. The leaf images were pre-processed and the features were extracted by using three different Convolutional Neural Network (CNN) models namely pre-trained AlexNet, fine-tuned AlexNet, and D-Leaf. These features were then classified by using five machine learning techniques, namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), k-Nearest-Neighbor (k-NN), Naïve-Bayes (NB), and CNN. A conventional morphometric method computed the morphological measurements based on the Sobel segmented veins was employed for benchmarking purposes. The D-Leaf model achieved a comparable testing accuracy of 94.88 percent as compared to AlexNet (93.26 percent) and fine-tuned AlexNet (95.54 percent) models. In addition, CNN models performed better than the traditional morphometric measurements (66.55 percent). The features extracted from the CNN are found to be fitted well with the ANN classifier. D-Leaf can be an effective automated system for plant species identification as shown by the experimental results.
Learning Neural Representations for Network Anomaly Detection

This paper proposes latent representation models for improving network anomaly detection. Well-known anomaly detection algorithms often suffer from challenges posed by network data, such as high dimension and sparsity, and a lack of anomaly data for training, model selection, and hyperparameter tuning. Our approach is to introduce new regularizers to a classical autoencoder (AE) and a variational AE, which force normal data into a very tight area centered at the origin in the nonsaturating area of the bottleneck unit activations. These trained AEs on normal data will push normal points toward the origin, whereas anomalies, which differ from normal data, will be put far away from the normal region. The models are very different from common regularized AEs, sparse AE, and contractive AE, in which the regularized AEs tend to make their latent representation less sensitive to changes of the input data. The bottleneck feature space is now used as a new data representation. A number of one-class learning algorithms are used for evaluating the proposed models. The experiments testify that our models help these classifiers to perform efficiently and consistently on high-dimensional and sparse network datasets, even with relatively few training points. More importantly, the models can minimize the effect of model selection on these classifiers since their performance is insensitive to a wide range of hyperparameter settings.