Miracle-dz/UniFAR-FTUs huggingface dataset:
Establishing the Therapeutic Index of Fluid Resuscitation in the Septic Patient: A Narrative Review and Meta‐Analysis

This comprehensive review comparatively evaluates the safety and benefits of parenteral fluids used in resuscitation with a focus on sepsis. It also provides a random‐effects meta‐analysis of studies comparing restrictive resuscitation and usual care in sepsis with the primary outcome of mortality. In the septic patient, fluid therapy remains a complex interplay between fluid compartments in the body, the integrity of the endothelial barrier, and the inflammatory tone of the patient. Recent data have emerged describing the pharmacokinetics of fluid resuscitation that can be affected by the factors just listed, as well as mean arterial pressure, rate of infusion, volume of fluid infusate, nature of the fluid, and drug interactions. Fluid overload in sepsis has been associated with vasodilation, kidney injury, and increased mortality.
Bandwidth Usage Forecasting and Network Anomaly Detection Based on Neural Network Approach

The past few years have witnessed an impressive increasing number of online applications and services that play major roles of our daily life. This have created a growing need to improve our online networks performance and their immunity to online network attacks and anomalies. One approach to improve the network's performance is to predict future bandwidth demands. With accurate forecasting and comparing the forecasted network with real time bandwidth measurement, network management techniques such as resource allocation, traffic engineering, and detection of anomalies become less complex. To this end, a two-stage network analysis model is proposed. In the first stage, an Artificial Neural Network (ANN) is used to predict future bandwidth demands. The ANN is trained using a set of data collected from a local network over the period of five months. The forecasting ANN is tested for different prediction periods. The results are compared to real-time bandwidth measurements. The comparison shows that the forecasting ANN achieves an accuracy level of at least 96.7% for normal network operation. In the second stage, another ANN is used to classify the behavior of the network as normal or anomaly. In this work, the anomalies considered are Distributed Denial of Service (DDoS) attacks and link failures. Experimental DDoS attacks and link failures were performed to measure the network's behavior. The ANN in the second stage is trained and tested using the difference between the forecasted and real time bandwidth for different periods of time. The results show that the classifier is able to detect network anomalies with an error at most 0.6%.
Is quantitative flow ratio enough to accurately assess intermediate coronary stenosis? A comparison study with fractional flow reserve

Fractional flow reserve (FFR) is a recommend-ed tool to assess the hemodynamic relevance of borderline stenosis of epicardial coronary arteries but requires costly pressure wires and administration of a hyperemic agent [1]. A novel approach enabling rapid computation of FFR pullbacks from three-dimensional quantitative coronary angiography (3D QCA) has recently been developed [2, 3]. The computational FFR, known as quantitative flow ratio (QFR), may be obtained from 3D QCA using an advanced computer algorithms [2]. However, so far, data on the clinical performance of QFR are rather limited. Thus, the aim herein, was to assess the accuracy of QFR and correlation between QFR and FFR in the assessment of borderline coronary artery stenoses. Consecutive patients with stable angina, who were scheduled for FFR, were prospectively enrolled. Ethics approval was granted by the institutional ethics review process. Details of FFR procedure were previously described [4, 5]. Computation of QFR was performed offline, using a software (Medis Suite 2.1.12.2, Medis Medical Imaging System, Leiden, the Netherlands) by two independent corelab analyzers who were blinded to FFR results. The analysis was conducted by each analyzer and the mean value (from four calculations) was used for further analysis. The computed
Comprehensive Event Storyline Generation from Microblogs

Microblogging data contains a wealth of information of trending events and has gained increased attention among users, organizations, and research scholars for social media mining in different disciplines. Event storyline generation is one typical task of social media mining, whose goal is to extract the development stages with associated description of events. Existing storyline generation methods either generate storyline with less integrity or fail to guarantee the coherence between the discovered stages. Secondly, there are no scientific method to evaluate the quality of the storyline. In this paper, we propose a comprehensive storyline generation framework to address the above disadvantages. Given Microblogging data related to the specified event, we first propose Hot-Word-Based stage detection algorithm to identify the potential stages of event, which can effectively avoid ignoring important stages and preventing inconsistent sequence between stages. Community detection algorithm is applied then to select representative data for each stage. Finally, we conduct graph optimization algorithm to generate the logically coherent storylines of the event. We also introduce a new evaluation metric, SLEU, to emphasize the importance of the integrity and coherence of the generated storyline. Extensive experiments on real-world Chinese microblogging data demonstrate the effectiveness of the proposed methods in each module and the overall framework.
Performance Analysis of Deep Learning Network Models of Localized Images in Chest X-ray Decision Support System

Nowadays, the implementation of deep learning in various fields of data mining and big data analytics has been widely used in many applications. This happens because of the ability of deep learning to be able to have excellent performance in terms of predicting cases of classification, features engineering, and clustering. Images data or texts that have large dimensions can also be processed iteratively by deep learning. In the medical field, deep learning is widely used as computer-aided detection (CAD) to provide a decision support system for radiologists or practitioners. In this research, we try to do a deep performance evaluation of several deep learning network models for the Chest X-ray disease decision support system (DSS). These X-ray images are enormous, it consists of 110,120 images, and the size is about 44 GB. Our primary interest is to get a detailed performance profile for every deep learning network model. It consists of a few evaluation aspects, accuracy performance by using areas under the receiver operating characteristic (AUROC), evaluation of training and testing time, investigation of memory usage, observation of central processing unit (CPU) usage, graphics processor power consumption, and provide some improvement solutions. We also offered a few solutions and suggestions to help the doctor or practitioner to select the most effective deep learning network model.
Incidence of Citrus leprosis virus C and Orchid fleck dichorhavirus Citrus Strain in Mites of the Genus Brevipalpus in Mexico

Abstract The incidences of Citrus leprosis virus C (CiLV-C) and Orchid fleck dichorhavirus Citrus strain (OFV-citrus) were determined in field populations of Brevipalpus mites from 15 citrus-producing states in Mexico. Mites were collected from orange, grapefruit, mandarin, lime, and sweet lime orchards. Brevipalpus yothersi (Baker) (Trombidiformes: Tenuipalpidae) was the most abundant species followed by Brevipalpus californicus (Banks) (Trombidiformes: Tenuipalpidae), which confirmed previous reports. The viruses CiLV-C and OFV-citrus were found in both mite species. The incidence of CiLV-C, OFV-citrus and both viruses simultaneously (CiLV-C and OFV-citrus) was 17.2, 10.3, and 3.4% (n = 116) for B. yothersi, and 12.5, 20.8, and 4.1% (n = 24) for B. californicus, respectively. No significant difference was found when the incidence of these viruses was compared between both mite species. The importance of our results in relation to the epidemiology of leprosis is discussed.
In Vitro High-Content Imaging-Based Phenotypic Analysis of Bronchial 3D Organotypic Air–Liquid Interface Cultures

High-content imaging (HCI) is a powerful method for quantifying biological effects in vitro. Historically, HCI has been applied to adherent cells growing in monolayers. With the advent of confocal versions of HCI devices, researchers now have the option of performing analyses on 3D cell cultures. However, some obstacles remain in integrating the third dimension, such as limited light penetration and less sophisticated image analysis. Here, we report the development of an HCI technique for imaging human bronchial 3D organotypic air–liquid interface (ALI) cultures (hBR-ALI). In this method, we monitored differentiation status through HCI evaluation markers representative of ciliated epithelial cells and goblet cells (Muc5AC [mucin 5AC]). As a second use case for demonstrating the utility of this technique, we induced goblet cell hyperplasia in hBR-ALI by using interleukin (IL)-13. Our results demonstrate the utility of the HCI technique for imaging hBR-ALI grown on Transwell inserts. This technique may be expanded to other cell culture systems, such as skin epithelia and 3D intestinal systems.
Temporal Prediction of Socio-economic Indicators Using Satellite Imagery

Machine learning models based on satellite data have been actively researched to serve as a proxy for the prediction of socio-economic development indicators. Such models have however rarely been tested for transferability over time, i.e. whether models learned on data for a certain year are able to make accurate predictions on data for another year. Using a dataset from the Indian census at two time points, for the years 2001 and 2011, we evaluate the temporal transferability of a simple machine learning model at sub-national scales of districts and propose a generic method to improve its performance. This method can be especially relevant when training datasets are small to train a robust prediction model. Then, we go further to build an aggregate development index at the district-level, on the lines of the Human Development Index (HDI) and demonstrate high accuracy in predicting the index based on satellite data for different years. This can be used to build applications to guide data-driven policy making at fine spatial and temporal scales, without the need to conduct frequent expensive censuses and surveys on the ground.
Stop Hiding Behind Windshield: A Windshield Image Enhancer Based on a Two-way Generative Adversarial Network

Windshield images captured by surveillance cameras are usually difficult to be seen through due to severe image degradation such as reflection, motion blur, low light, haze, and noise. Such image degradation hinders the capability of identifying and tracking people. In this paper, we aim to address this challenging windshield images enhancement task by presenting a novel deep learning model based on a two-way generative adversarial network, called Two-way Individual Normalization Perceptual Adversarial Network, TWIN-PAN. TWIN-PAN is an unpaired learning network which does not require pairs of degraded and corresponding ground truth images for training. Also, unlike existing image restoration algorithms which only address one specific type of degradation at once, TWIN-PAN can restore the image from various types of degradation. To restore the content inside the extremely degraded windshield and ensure the semantic consistency of the image, we introduce cyclic perceptual loss to the network and combine it with cycle-consistency loss. Moreover, to generate better restoration images, we introduce individual instance normalization layers for the generators, which can help our generators better adapt to their own input distributions. Furthermore, we collect a large high-quality windshield image dataset (WIE-Dataset) to train our network and to validate the robustness of our method in restoring degraded windshield images. Experimental results on human detection, vehicle ReID and user study manifest that the proposed method is effective for windshield image restoration.
Microenvironment‐induced TIMP2 loss by cancer‐secreted exosomal miR‐4443 promotes liver metastasis of breast cancer

We aimed to investigate the role of exosomal miR‐4443 in metastasis of breast cancer (BCa). In vitro wound‐healing assay and transwell invasion assay were used to investigate effect of miR‐4443 on BCa cells. Animal experiments were performed to confirm its effects in vivo. miR‐4443 promotes the metastasis of BCa cells through downregulating tissue inhibitors of metalloproteinase 2 (TIMP2) and upregulating matrix metalloproteinases (MMPs). Highly invasive BCa cells have a higher expression of miR‐4443 in both cells and exosomes. The exosomes derived from highly invasive BCa cells mainly gather in the primary tumor and liver. In vivo, overexpression of miR‐4443 in noninvasive BCa cells induces liver metastasis, accompanied with downregulated TIMP2, and upregulated MMP‐2 in both the primary tumor and liver. When we armed MCF‐10A exosomes with miR‐4443 inhibitors to treat mice bearing high‐miR‐4443 tumors, exosomes accumulated in the primary tumor, and liver following the upregulation of TIMP2 and downregulation of MMP2, and the metastasis was inhibited. Highly invasive BCa cells destroy natural barriers against metastasis by delivering exosomal miR‐4443 to stromal cells of the primary tumor and impairing TIMP2, consequently activating MMP; circulating exosomal miR‐4443 might promote BCa cells lodging in future metastatic sites through the similar mechanisms.
The therapeutic effect and nusing experience of the fire-needle joint Tazarotene Gel in treating verruca plana

Objetive To observe both the therapeutic and nusing experience of the fire-needle joint Tazarotene Gel in treating verruca plana. Methods A total of 92 subjects with verruca plana were randomized into two groups(46cases each one). The subjects in the treatment group received fireneedle and Tazarotene,gived the related nusing,and the control group was treated by Tazarotene Gel only.The clinical efficacy was compared between the two groups. Results The total efficacy rate was89.13% respectively in the reatment group and 67.39% respectively,in the control group.T here was a significant difference between the two groups(P0.05). Conclusion Fire-needle joint Tazarotene Gel and the related nusing had a good clinical effect on verruca plana.It is worthy of clinical promotion.
Enhancing KCNQ Channel Activity Improves Neurobehavioral Recovery after Spinal Cord Injury

Spinal cord injury (SCI) usually leads to acute neuronal death and delayed secondary degeneration, resulting in sensory dysfunction, paralysis, and chronic pain. Excessive excitation is one of the critical factors leading to secondary neural damage initiated by various insults. KCNQ/Kv7 channels are highly expressed in spinal neurons and axons and play an important role in controlling their excitability. Enhancing KCNQ channel activity by using its specific opener retigabine could thus be a plausible treatment strategy to reduce the pathology after SCI. We produced contusive SCI at T10 in adult male rats, which then received 10 consecutive days’ treatment with retigabine or vehicle starting 3 hours or 3 days after contusion. Two different concentrations and two different delivery methods were applied. Delivery of retigabine via Alzet osmotic pumps, but not intraperitoneal injections 3 hours after contusion, promoted recovery of locomotor function. Remarkably, retigabine delivery in both methods significantly attenuated the development of mechanical stimuli–induced hyperreflexia and spontaneous pain; however, no significant difference in the thermal threshold was observed. Although retigabine delivered 3 days after contusion significantly attenuated the development of mechanical hypersensitivity and spontaneous pain, the locomotor function is not improved by the delayed treatments. Finally, we found that early application of retigabine attenuates the inflammatory activity in the spinal cord and increases the survival of white matter after SCI. Our results suggest that decreasing neuronal excitability by targeting KCNQ/Kv7 channels at acute stage aids the recovery of locomotor function and attenuates the development of neuropathic pain after SCI. SIGNIFICANCE STATEMENT Several pharmacological interventions have been proposed for spinal cord injury (SCI) treatment, but none have been shown to be both effective and safe in clinical trials. Necrotic neuronal death and chronic pain are often the cost of pathological neural excitation after SCI. We show that early, brief application of retigabine could aid locomotor and sensory neurobehavioral recovery after SCI, supporting the use of this drug in the clinic to promote motor and sensory function in patients with SCI.
Assaying Chromatin Accessibility Using ATAC-Seq in Invertebrate Chordate Embryos

Cis-regulatory elements (CREs) are non-coding DNA regions involved in the spatio-temporal regulation of gene expression. Gene regulatory changes drive animal development and play major roles during evolution of animal body plans. Therefore, we believe that determining CREs at different developmental stages and across animal lineages is critical to understand how evolution operates through development. The Assay for Transposase-Accessible Chromatin followed by high-throughput sequencing (ATAC-seq) is a powerful technique for the study of CREs that takes advantage of Tn5 transposase activity. Starting from fewer than 105 cells, in a 1-day procedure, it is possible to detect, at a genome-wide level, CREs located in open chromatin regions with high resolution. Here, we describe a detailed step-by-step ATAC-seq protocol for invertebrate chordate marine embryos. We have successfully applied this technique to amphioxus and two species of tunicate embryos. We also show an easy workflow to analyze data generated with this technique. Moreover, we point out that this method and our bioinformatic pipeline are efficient to detect CREs associated with Wnt signaling pathway by simply using embryos treated with a drug that perturbs this pathway. This approach can be extended to other signaling pathways and also to embryo mutants for critical genes. Our results therefore demonstrate the power of ATAC-seq for the identification of CREs that play essential functions during animal development in a wide range of invertebrate or vertebrate animals.
The Use of Point‐of‐Care Ultrasound to Evaluate Pulsus Paradoxus in Children With Asthma

Pulsus paradoxus (PP) is defined as a fall of systolic blood pressure of greater than 10 mm Hg during the inspiratory phase of respiration. Measurement of PP is recommended by national and international asthma guidelines as an objective measure of asthma severity but is rarely used in clinical practice. Cardiac point‐of‐care ultrasound with pulsed wave Doppler imaging measuring respiratory‐phasic changes of mitral valve inflow velocities is well described in cardiac tamponade as “sonographic” PP. We present 10 cases of acute asthma presenting to an emergency department showing the finding of sonographic determined PP in the apical 4‐chamber view of the heart on pulsed wave Doppler imaging.
An Improved Hybrid Segmentation Method for Remote Sensing Images

Image segmentation technology, which can be used to completely partition a remote sensing image into non-overlapping regions in the image space, plays an indispensable role in high-resolution remote sensing image classification. Recently, the segmentation methods that combine segmenting with merging have attracted researchers’ attention. However, the existing methods ignore the fact that the same parameters must be applied to every segmented geo-object, and fail to consider the homogeneity between adjacent geo-objects. This paper develops an improved remote sensing image segmentation method to overcome this limitation. The proposed method is a hybrid method (split-and-merge). First, a watershed algorithm based on pre-processing is used to split the image to form initial segments. Second, the fast lambda-schedule algorithm based on a common boundary length penalty is used to merge the initial segments to obtain the final segmentation. For this experiment, we used GF-1 images with three spatial resolutions: 2 m, 8 m and 16 m. Six different test areas were chosen from the GF-1 images to demonstrate the effectiveness of the improved method, and the objective function (F (v, I)), intrasegment variance (v) and Moran’s index were used to evaluate the segmentation accuracy. The validation results indicated that the improved segmentation method produced satisfactory segmentation results for GF-1 images (average F (v, I) = 0.1064, v = 0.0428 and I = 0.17).
Incarceration and Number of Sexual Partners After Incarceration Among Vulnerable US Women, 2007-2017.

Objectives. To examine whether women's incarceration increases numbers of total and new sexual partners.Methods. US women with or at risk for HIV in a multicenter cohort study answered incarceration and sexual partner questions semiannually between 2007 and 2017. We used marginal structural models to compare total and new partners at visits not following incarceration with all visits following incarceration and visits immediately following incarceration. Covariates included demographics, HIV status, sex exchange, drug or alcohol use, and housing instability.Results. Of the 3180 participants, 155 were incarcerated. Women reported 2 partners, 3 or more partners, and new partners at 5.2%, 5.2%, and 9.3% of visits, respectively. Relative to visits not occurring after incarceration, odds ratios were 2.41 (95% confidence interval [CI] = 1.20, 4.85) for 2 partners, 2.03 (95% CI = 0.97, 4.26) for 3 or more partners, and 3.24 (95% CI = 1.69, 6.22) for new partners at visits immediately after incarceration. Odds ratios were similar for all visits following incarceration.Conclusions. Women had more total partners and new partners immediately and at all visits following incarceration after confounders and loss to follow-up had been taken into account.
Editorial to Temporal Gut Microbial Changes Predict Recurrent Clostridium difficile Infection in Patients With and Without Ulcerative Colitis.

The rate of Clostridium difficile infection (CDI) has dra-matically increased over the last 2 decades and has emerged as a global public health threat. It is one of the leading health care–associated infections and is a significant economic burden in North America. 1 Symptoms range from mild to severe diarrhea, toxic megacolon, or even death. 2,3 There are many risk factors for the development of CDI, all which lead to dysbiosis or alterations in the intestinal microbiome. Patients with inflammatory bowel disease (IBD) are particularly at risk for CDI and recurrent CDI (rCDI). 4,5 The clinical presentation of CDI in patients with IBD can be atypical, including pre-senting at a younger age, a lack of antibiotic exposure, and community onset. The sequelae of infection in patients with IBD can include toxic megacolon, sepsis, colectomy, and death. 6 In addition, these patients are at risk for exacerbation of their underlying IBD, and CDI can change the trajectory